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+WEBVTT
+
+00:00:01.280 --> 00:00:06.759
+so the class today is uh introduction to
+
+00:00:04.680 --> 00:00:09.480
+natural language processing and I'll be
+
+00:00:06.759 --> 00:00:11.200
+talking a little bit about you know what
+
+00:00:09.480 --> 00:00:14.719
+is natural language processing why we're
+
+00:00:11.200 --> 00:00:16.720
+motivated to do it and also some of the
+
+00:00:14.719 --> 00:00:18.039
+difficulties that we encounter and I'll
+
+00:00:16.720 --> 00:00:19.880
+at the end I'll also be talking about
+
+00:00:18.039 --> 00:00:22.519
+class Logistics so you can ask any
+
+00:00:19.880 --> 00:00:25.439
+Logistics questions at that
+
+00:00:22.519 --> 00:00:27.720
+time so if we talk about what is NLP
+
+00:00:25.439 --> 00:00:29.320
+anyway uh does anyone have any opinions
+
+00:00:27.720 --> 00:00:31.439
+about the definition of what natural
+
+00:00:29.320 --> 00:00:33.239
+language process would be oh one other
+
+00:00:31.439 --> 00:00:35.680
+thing I should mention is I am recording
+
+00:00:33.239 --> 00:00:38.600
+the class uh I put the class on YouTube
+
+00:00:35.680 --> 00:00:40.520
+uh afterwards I will not take pictures
+
+00:00:38.600 --> 00:00:41.920
+or video of any of you uh but if you
+
+00:00:40.520 --> 00:00:44.719
+talk your voice might come in the
+
+00:00:41.920 --> 00:00:47.440
+background so just uh be aware of that
+
+00:00:44.719 --> 00:00:49.000
+um usually not it's a directional mic so
+
+00:00:47.440 --> 00:00:51.559
+I try to repeat the questions after
+
+00:00:49.000 --> 00:00:54.079
+everybody um but uh for the people who
+
+00:00:51.559 --> 00:00:57.680
+are recordings uh listening to the
+
+00:00:54.079 --> 00:00:59.320
+recordings um so anyway what is NLP
+
+00:00:57.680 --> 00:01:03.120
+anyway does anybody have any ideas about
+
+00:00:59.320 --> 00:01:03.120
+the definition of what NLP might
+
+00:01:06.119 --> 00:01:09.119
+be
+
+00:01:15.439 --> 00:01:21.759
+yes okay um it so the answer was it
+
+00:01:19.240 --> 00:01:25.759
+helps machines understand language
+
+00:01:21.759 --> 00:01:27.920
+better uh so to facilitate human human
+
+00:01:25.759 --> 00:01:31.159
+and human machine interactions I think
+
+00:01:27.920 --> 00:01:32.759
+that's very good um it's
+
+00:01:31.159 --> 00:01:36.520
+uh similar to what I have written on my
+
+00:01:32.759 --> 00:01:38.040
+slide here uh but natur in addition to
+
+00:01:36.520 --> 00:01:41.280
+natural language understanding there's
+
+00:01:38.040 --> 00:01:46.000
+one major other segment of NLP uh does
+
+00:01:41.280 --> 00:01:46.000
+anyone uh have an idea what that might
+
+00:01:48.719 --> 00:01:53.079
+be we often have a dichotomy between two
+
+00:01:51.399 --> 00:01:55.240
+major segments natural language
+
+00:01:53.079 --> 00:01:57.520
+understanding and natural language
+
+00:01:55.240 --> 00:01:59.439
+generation yeah exactly so I I would say
+
+00:01:57.520 --> 00:02:03.119
+that's almost perfect if you had said
+
+00:01:59.439 --> 00:02:06.640
+understand and generate so very good um
+
+00:02:03.119 --> 00:02:08.560
+so I I say natural technology to handle
+
+00:02:06.640 --> 00:02:11.400
+human language usually text using
+
+00:02:08.560 --> 00:02:13.200
+computers uh to Aid human machine
+
+00:02:11.400 --> 00:02:15.480
+communication and this can include
+
+00:02:13.200 --> 00:02:17.879
+things like question answering dialogue
+
+00:02:15.480 --> 00:02:20.840
+or generation of code that can be
+
+00:02:17.879 --> 00:02:23.239
+executed with uh
+
+00:02:20.840 --> 00:02:25.080
+computers it can also Aid human human
+
+00:02:23.239 --> 00:02:27.440
+communication and this can include
+
+00:02:25.080 --> 00:02:30.440
+things like machine translation or spell
+
+00:02:27.440 --> 00:02:32.640
+checking or assisted writing
+
+00:02:30.440 --> 00:02:34.560
+and then a final uh segment that people
+
+00:02:32.640 --> 00:02:37.400
+might think about a little bit less is
+
+00:02:34.560 --> 00:02:39.400
+analyzing and understanding a language
+
+00:02:37.400 --> 00:02:42.400
+and this includes things like syntactic
+
+00:02:39.400 --> 00:02:44.959
+analysis text classification entity
+
+00:02:42.400 --> 00:02:47.400
+recognition and linking and these can be
+
+00:02:44.959 --> 00:02:49.159
+used for uh various reasons not
+
+00:02:47.400 --> 00:02:51.000
+necessarily for direct human machine
+
+00:02:49.159 --> 00:02:52.720
+communication but also for like
+
+00:02:51.000 --> 00:02:54.400
+aggregating information across large
+
+00:02:52.720 --> 00:02:55.760
+things for scientific studies and other
+
+00:02:54.400 --> 00:02:57.519
+things like that I'll give a few
+
+00:02:55.760 --> 00:03:00.920
+examples of
+
+00:02:57.519 --> 00:03:04.040
+this um we now use an many times a day
+
+00:03:00.920 --> 00:03:06.480
+sometimes without even knowing it so uh
+
+00:03:04.040 --> 00:03:09.400
+whenever you're typing a doc in Google
+
+00:03:06.480 --> 00:03:11.599
+Docs there's you know spell checking and
+
+00:03:09.400 --> 00:03:13.959
+grammar checking going on behind it's
+
+00:03:11.599 --> 00:03:15.920
+gotten frighten frighteningly good
+
+00:03:13.959 --> 00:03:18.280
+recently that where it checks like most
+
+00:03:15.920 --> 00:03:20.720
+of my mistakes and rarely Flags things
+
+00:03:18.280 --> 00:03:22.799
+that are not mistakes so obviously they
+
+00:03:20.720 --> 00:03:25.080
+have powerful models running behind that
+
+00:03:22.799 --> 00:03:25.080
+uh
+
+00:03:25.640 --> 00:03:33.080
+so and it can do things like answer
+
+00:03:28.720 --> 00:03:34.599
+questions uh so I asked chat GPT who is
+
+00:03:33.080 --> 00:03:37.000
+the current president of Carnegie melan
+
+00:03:34.599 --> 00:03:38.920
+University and chat GPT said I did a
+
+00:03:37.000 --> 00:03:40.920
+quick search for more information here
+
+00:03:38.920 --> 00:03:43.439
+is what I found uh the current president
+
+00:03:40.920 --> 00:03:47.120
+of car Mel University is faram Janan he
+
+00:03:43.439 --> 00:03:50.040
+has been serving since July 1 etc etc so
+
+00:03:47.120 --> 00:03:50.040
+as far as I can tell that's
+
+00:03:50.400 --> 00:03:56.319
+correct um at the same time I asked how
+
+00:03:53.799 --> 00:04:00.280
+many layers are included in the GP 3.5
+
+00:03:56.319 --> 00:04:02.360
+turbo architecture and it said to me
+
+00:04:00.280 --> 00:04:05.400
+GPT 3.5 turbo which is an optimized
+
+00:04:02.360 --> 00:04:07.239
+version of GPT 3.5 for faster responses
+
+00:04:05.400 --> 00:04:08.959
+doesn't have a specific layer art
+
+00:04:07.239 --> 00:04:11.720
+structure like the traditional gpt3
+
+00:04:08.959 --> 00:04:13.560
+models um and I don't know if this is
+
+00:04:11.720 --> 00:04:16.600
+true or not but I'm pretty sure it's not
+
+00:04:13.560 --> 00:04:18.840
+true I'm pretty sure that you know GPT
+
+00:04:16.600 --> 00:04:20.560
+is a model that's much like other models
+
+00:04:18.840 --> 00:04:21.560
+uh so it basically just made up the spec
+
+00:04:20.560 --> 00:04:22.880
+because it didn't have any information
+
+00:04:21.560 --> 00:04:26.000
+on the Internet or couldn't talk about
+
+00:04:22.880 --> 00:04:26.000
+it so
+
+00:04:26.120 --> 00:04:33.479
+um another thing is uh NLP can translate
+
+00:04:29.639 --> 00:04:37.759
+text pretty well so I ran um Google
+
+00:04:33.479 --> 00:04:39.560
+translate uh on Japanese uh this example
+
+00:04:37.759 --> 00:04:41.639
+is a little bit old it's from uh you
+
+00:04:39.560 --> 00:04:44.639
+know a few years ago about Co but I I
+
+00:04:41.639 --> 00:04:46.240
+retranslated it a few days ago and it
+
+00:04:44.639 --> 00:04:47.680
+comes up pretty good uh you can
+
+00:04:46.240 --> 00:04:49.639
+basically understand what's going on
+
+00:04:47.680 --> 00:04:53.520
+here it's not perfect but you can
+
+00:04:49.639 --> 00:04:56.400
+understand the uh the general uh
+
+00:04:53.520 --> 00:04:58.560
+gist at the same time uh if I put in a
+
+00:04:56.400 --> 00:05:02.280
+relatively low resource language this is
+
+00:04:58.560 --> 00:05:05.759
+Kurdish um it has a number of problems
+
+00:05:02.280 --> 00:05:08.199
+when you try to understand it and just
+
+00:05:05.759 --> 00:05:12.400
+to give an example this is talking about
+
+00:05:08.199 --> 00:05:14.320
+uh some uh paleontology Discovery it
+
+00:05:12.400 --> 00:05:15.800
+called this person a fossil scientist
+
+00:05:14.320 --> 00:05:17.440
+instead of the kind of obvious English
+
+00:05:15.800 --> 00:05:20.120
+term
+
+00:05:17.440 --> 00:05:23.520
+paleontologist um and it's talking about
+
+00:05:20.120 --> 00:05:25.240
+three different uh T-Rex species uh how
+
+00:05:23.520 --> 00:05:27.039
+T-Rex should actually be split into
+
+00:05:25.240 --> 00:05:29.639
+three species where T-Rex says king of
+
+00:05:27.039 --> 00:05:31.560
+ferocious lizards emperator says emperor
+
+00:05:29.639 --> 00:05:33.720
+of Savaged lizards and then T Regina
+
+00:05:31.560 --> 00:05:35.120
+means clean of ferocious snail I'm
+
+00:05:33.720 --> 00:05:37.240
+pretty sure that's not snail I'm pretty
+
+00:05:35.120 --> 00:05:41.080
+sure that's lizard so uh you can see
+
+00:05:37.240 --> 00:05:41.080
+that this is not uh this is not perfect
+
+00:05:41.280 --> 00:05:46.680
+either some people might be thinking why
+
+00:05:43.960 --> 00:05:48.400
+Google translate and why not GPD well it
+
+00:05:46.680 --> 00:05:49.960
+turns out um according to one of the
+
+00:05:48.400 --> 00:05:51.759
+recent studies we've done GPD is even
+
+00:05:49.960 --> 00:05:55.479
+worse at these slow resource languages
+
+00:05:51.759 --> 00:05:58.120
+so I use the best thing that's out
+
+00:05:55.479 --> 00:06:00.440
+there um another thing is language
+
+00:05:58.120 --> 00:06:02.039
+analysis can Aid scientific ific inquiry
+
+00:06:00.440 --> 00:06:03.600
+so this is an example that I've been
+
+00:06:02.039 --> 00:06:06.120
+using for a long time it's actually from
+
+00:06:03.600 --> 00:06:09.160
+Martin sap another faculty member here
+
+00:06:06.120 --> 00:06:12.440
+uh but I have been using it since uh
+
+00:06:09.160 --> 00:06:14.160
+like before he joined and it uh this is
+
+00:06:12.440 --> 00:06:16.039
+an example from computational social
+
+00:06:14.160 --> 00:06:18.599
+science uh answering questions about
+
+00:06:16.039 --> 00:06:20.240
+Society given observational data and
+
+00:06:18.599 --> 00:06:22.280
+their question was do movie scripts
+
+00:06:20.240 --> 00:06:24.599
+portray female or male characters with
+
+00:06:22.280 --> 00:06:27.520
+more power or agency in movie script
+
+00:06:24.599 --> 00:06:30.120
+films so it's asking kind of a so
+
+00:06:27.520 --> 00:06:32.160
+societal question by using NLP
+
+00:06:30.120 --> 00:06:35.360
+technology and the way they did it is
+
+00:06:32.160 --> 00:06:36.880
+they basically analyzed text trying to
+
+00:06:35.360 --> 00:06:43.080
+find
+
+00:06:36.880 --> 00:06:45.280
+uh the uh agents and patients in a a
+
+00:06:43.080 --> 00:06:46.479
+particular text which are the the things
+
+00:06:45.280 --> 00:06:49.280
+that are doing things and the things
+
+00:06:46.479 --> 00:06:52.639
+that things are being done to and you
+
+00:06:49.280 --> 00:06:54.440
+can see that essentially male characters
+
+00:06:52.639 --> 00:06:56.560
+in these movie scripts were given more
+
+00:06:54.440 --> 00:06:58.080
+power in agency and female characters
+
+00:06:56.560 --> 00:06:59.960
+were given less power in agency and they
+
+00:06:58.080 --> 00:07:02.680
+were able to do this because they had
+
+00:06:59.960 --> 00:07:04.840
+NLP technology that analyzed and
+
+00:07:02.680 --> 00:07:08.960
+extracted useful data and made turned it
+
+00:07:04.840 --> 00:07:11.520
+into a very easy form to do kind of
+
+00:07:08.960 --> 00:07:15.840
+analysis of the variety that they want
+
+00:07:11.520 --> 00:07:17.400
+so um I think that's a major use case of
+
+00:07:15.840 --> 00:07:19.400
+NLP technology that does language
+
+00:07:17.400 --> 00:07:20.919
+analysis nowadays turn it into a form
+
+00:07:19.400 --> 00:07:23.960
+that allows you to very quickly do
+
+00:07:20.919 --> 00:07:27.440
+aggregate queries and other things like
+
+00:07:23.960 --> 00:07:30.479
+this um but at the same time uh language
+
+00:07:27.440 --> 00:07:33.520
+analysis tools fail at very basic tasks
+
+00:07:30.479 --> 00:07:36.000
+so these are
+
+00:07:33.520 --> 00:07:38.199
+some things that I ran through a named
+
+00:07:36.000 --> 00:07:41.080
+entity recognizer and these were kind of
+
+00:07:38.199 --> 00:07:43.160
+very nice named entity recognizers uh
+
+00:07:41.080 --> 00:07:46.240
+that a lot of people were using for
+
+00:07:43.160 --> 00:07:48.039
+example Stanford core NLP and Spacey and
+
+00:07:46.240 --> 00:07:50.319
+both of them I just threw in the first
+
+00:07:48.039 --> 00:07:53.120
+thing that I found on the New York Times
+
+00:07:50.319 --> 00:07:55.199
+at the time and it basically made at
+
+00:07:53.120 --> 00:07:58.319
+least one mistake in the first sentence
+
+00:07:55.199 --> 00:08:00.840
+and here it recognizes Baton Rouge as an
+
+00:07:58.319 --> 00:08:04.720
+organization and here it recognized
+
+00:08:00.840 --> 00:08:07.000
+hurricane EA as an organization so um
+
+00:08:04.720 --> 00:08:08.879
+like even uh these things that we expect
+
+00:08:07.000 --> 00:08:10.360
+should work pretty well make pretty
+
+00:08:08.879 --> 00:08:13.360
+Solly
+
+00:08:10.360 --> 00:08:16.199
+mistakes so in the class uh basically
+
+00:08:13.360 --> 00:08:18.479
+what I want to cover is uh what goes
+
+00:08:16.199 --> 00:08:20.360
+into building uh state-of-the-art NLP
+
+00:08:18.479 --> 00:08:24.000
+systems that work really well on a wide
+
+00:08:20.360 --> 00:08:26.240
+variety of tasks um where do current
+
+00:08:24.000 --> 00:08:28.840
+systems
+
+00:08:26.240 --> 00:08:30.479
+fail and how can we make appropriate
+
+00:08:28.840 --> 00:08:35.000
+improvements and Achieve whatever we
+
+00:08:30.479 --> 00:08:37.719
+want to do with nalp and this set of
+
+00:08:35.000 --> 00:08:39.360
+questions that I'm asking here is
+
+00:08:37.719 --> 00:08:40.919
+exactly the same as the set of questions
+
+00:08:39.360 --> 00:08:43.519
+that I was asking two years ago before
+
+00:08:40.919 --> 00:08:45.480
+chat GPT uh I still think they're
+
+00:08:43.519 --> 00:08:46.920
+important questions but I think the
+
+00:08:45.480 --> 00:08:48.399
+answers to these questions is very
+
+00:08:46.920 --> 00:08:50.040
+different and because of that we're
+
+00:08:48.399 --> 00:08:52.120
+updating the class materials to try to
+
+00:08:50.040 --> 00:08:54.399
+cover you know the answers to these
+
+00:08:52.120 --> 00:08:56.000
+questions and uh in kind of the era of
+
+00:08:54.399 --> 00:08:58.200
+large language models and other things
+
+00:08:56.000 --> 00:08:59.720
+like
+
+00:08:58.200 --> 00:09:02.079
+that
+
+00:08:59.720 --> 00:09:03.360
+so that's all I have for the intro maybe
+
+00:09:02.079 --> 00:09:06.640
+maybe pretty straightforward are there
+
+00:09:03.360 --> 00:09:08.480
+any questions or comments so far if not
+
+00:09:06.640 --> 00:09:14.399
+I'll I'll just go
+
+00:09:08.480 --> 00:09:17.160
+on okay great so I want to uh first go
+
+00:09:14.399 --> 00:09:19.480
+into a very high Lev overview of NLP
+
+00:09:17.160 --> 00:09:20.839
+system building and most of the stuff
+
+00:09:19.480 --> 00:09:22.399
+that I want to do today is to set the
+
+00:09:20.839 --> 00:09:24.320
+stage for what I'm going to be talking
+
+00:09:22.399 --> 00:09:25.040
+about in more detail uh over the rest of
+
+00:09:24.320 --> 00:09:29.200
+the
+
+00:09:25.040 --> 00:09:31.720
+class and we could think of NLP syst
+
+00:09:29.200 --> 00:09:34.040
+systems through this kind of General
+
+00:09:31.720 --> 00:09:36.560
+framework where we want to create a
+
+00:09:34.040 --> 00:09:40.600
+function to map an input X into an
+
+00:09:36.560 --> 00:09:44.440
+output y uh where X and or Y involve
+
+00:09:40.600 --> 00:09:47.000
+language and uh do some people have
+
+00:09:44.440 --> 00:09:50.120
+favorite NLP tasks or NLP tasks that you
+
+00:09:47.000 --> 00:09:52.399
+want to uh want to be handling in some
+
+00:09:50.120 --> 00:09:57.000
+way or maybe what what do you think are
+
+00:09:52.399 --> 00:09:57.000
+the most popular and important NLP tasks
+
+00:09:58.120 --> 00:10:03.200
+nowadays
+
+00:10:00.800 --> 00:10:06.120
+okay so translation is maybe easy what's
+
+00:10:03.200 --> 00:10:06.120
+the input and output of
+
+00:10:11.440 --> 00:10:15.720
+translation okay yeah so uh in
+
+00:10:13.800 --> 00:10:17.959
+Translation inputs text in one language
+
+00:10:15.720 --> 00:10:21.760
+output is text in another language and
+
+00:10:17.959 --> 00:10:21.760
+then what what is a good
+
+00:10:27.680 --> 00:10:32.160
+translation yeah corre or or the same is
+
+00:10:30.320 --> 00:10:35.839
+the input basically yes um it also
+
+00:10:32.160 --> 00:10:37.760
+should be fluent but I agree any other
+
+00:10:35.839 --> 00:10:39.839
+things generation the reason why I said
+
+00:10:37.760 --> 00:10:41.519
+it's tough is it's pretty broad um and
+
+00:10:39.839 --> 00:10:43.360
+it's not like we could be doing
+
+00:10:41.519 --> 00:10:46.360
+generation with lots of different inputs
+
+00:10:43.360 --> 00:10:51.440
+but um yeah any any other things maybe a
+
+00:10:46.360 --> 00:10:51.440
+little bit different yeah like
+
+00:10:51.480 --> 00:10:55.959
+scenario a scenario and a multiple
+
+00:10:54.000 --> 00:10:58.200
+choice question about the scenario and
+
+00:10:55.959 --> 00:10:59.680
+so what would the scenario in the
+
+00:10:58.200 --> 00:11:01.760
+multiple choice question are probably
+
+00:10:59.680 --> 00:11:04.040
+the input and then the output
+
+00:11:01.760 --> 00:11:06.480
+is an answer to the multiple choice
+
+00:11:04.040 --> 00:11:07.920
+question um and then there it's kind of
+
+00:11:06.480 --> 00:11:12.279
+obvious like what is good it's the
+
+00:11:07.920 --> 00:11:14.880
+correct answer sure um interestingly I
+
+00:11:12.279 --> 00:11:17.440
+think a lot of llm evaluation is done on
+
+00:11:14.880 --> 00:11:21.160
+these multiple choice questions but I'm
+
+00:11:17.440 --> 00:11:22.320
+yet to encounter an actual application
+
+00:11:21.160 --> 00:11:24.880
+that cares about multiple choice
+
+00:11:22.320 --> 00:11:26.880
+question answering so uh there's kind of
+
+00:11:24.880 --> 00:11:30.959
+a funny disconnect there but uh yeah I
+
+00:11:26.880 --> 00:11:33.519
+saw hand that think about V search comp
+
+00:11:30.959 --> 00:11:36.360
+yeah Vector search uh that's very good
+
+00:11:33.519 --> 00:11:36.360
+so the input
+
+00:11:37.120 --> 00:11:45.000
+is can con it into or understanding and
+
+00:11:42.560 --> 00:11:45.000
+it to
+
+00:11:47.360 --> 00:11:53.760
+another okay yeah so I'd say the input
+
+00:11:49.880 --> 00:11:56.160
+there is a query and a document base um
+
+00:11:53.760 --> 00:11:57.959
+and then the output is maybe an index
+
+00:11:56.160 --> 00:11:59.800
+into the document or or something else
+
+00:11:57.959 --> 00:12:01.279
+like that sure um and then something
+
+00:11:59.800 --> 00:12:05.040
+that's good here here's a good question
+
+00:12:01.279 --> 00:12:05.040
+what what's a good result from
+
+00:12:06.560 --> 00:12:10.200
+that what's a good
+
+00:12:10.839 --> 00:12:19.279
+output be sort of simar the major
+
+00:12:15.560 --> 00:12:21.680
+problem there I see is how you def SAR
+
+00:12:19.279 --> 00:12:26.199
+and how you
+
+00:12:21.680 --> 00:12:29.760
+a always like you understand
+
+00:12:26.199 --> 00:12:33.000
+whether is actually
+
+00:12:29.760 --> 00:12:35.079
+yeah exactly so that um just to repeat
+
+00:12:33.000 --> 00:12:36.880
+it's like uh we need to have a
+
+00:12:35.079 --> 00:12:38.399
+similarity a good similarity metric we
+
+00:12:36.880 --> 00:12:40.120
+need to have a good threshold where we
+
+00:12:38.399 --> 00:12:41.760
+get like the ones we want and we don't
+
+00:12:40.120 --> 00:12:43.240
+get the ones we don't want we're going
+
+00:12:41.760 --> 00:12:44.959
+to talk more about that in the retrieval
+
+00:12:43.240 --> 00:12:48.440
+lecture exactly how we evaluate and
+
+00:12:44.959 --> 00:12:49.920
+stuff but um yeah good so this is a good
+
+00:12:48.440 --> 00:12:53.279
+uh here are some good examples I have
+
+00:12:49.920 --> 00:12:55.519
+some examples of my own um the first one
+
+00:12:53.279 --> 00:12:58.360
+is uh kind of the very generic one maybe
+
+00:12:55.519 --> 00:13:00.800
+kind of like generation here but text in
+
+00:12:58.360 --> 00:13:02.959
+continuing text uh so this is language
+
+00:13:00.800 --> 00:13:04.160
+modeling so you have a text and then you
+
+00:13:02.959 --> 00:13:05.440
+have the continuation you want to
+
+00:13:04.160 --> 00:13:07.680
+predict the
+
+00:13:05.440 --> 00:13:10.480
+continuation um text and text in another
+
+00:13:07.680 --> 00:13:13.040
+language is translation uh text in a
+
+00:13:10.480 --> 00:13:15.800
+label could be text classification uh
+
+00:13:13.040 --> 00:13:17.760
+text in linguistic structure or uh some
+
+00:13:15.800 --> 00:13:21.360
+s kind of entities or something like
+
+00:13:17.760 --> 00:13:22.680
+that could be uh language analysis or um
+
+00:13:21.360 --> 00:13:24.839
+information
+
+00:13:22.680 --> 00:13:29.440
+extraction uh we could also have image
+
+00:13:24.839 --> 00:13:31.320
+and text uh which is image captioning um
+
+00:13:29.440 --> 00:13:33.560
+or speech and text which is speech
+
+00:13:31.320 --> 00:13:35.240
+recognition and I take the very broad
+
+00:13:33.560 --> 00:13:38.000
+view of natural language processing
+
+00:13:35.240 --> 00:13:39.519
+which is if it's any variety of language
+
+00:13:38.000 --> 00:13:41.519
+uh if you're handling language in some
+
+00:13:39.519 --> 00:13:42.800
+way it's natural language processing it
+
+00:13:41.519 --> 00:13:45.880
+doesn't necessarily have to be text
+
+00:13:42.800 --> 00:13:47.480
+input text output um so that's relevant
+
+00:13:45.880 --> 00:13:50.199
+for the projects that you're thinking
+
+00:13:47.480 --> 00:13:52.160
+about too at the end of this course so
+
+00:13:50.199 --> 00:13:55.519
+the the most common FAQ for this course
+
+00:13:52.160 --> 00:13:57.839
+is does my project count and if you're
+
+00:13:55.519 --> 00:13:59.360
+uncertain you should ask but usually
+
+00:13:57.839 --> 00:14:01.040
+like if it has some sort of language
+
+00:13:59.360 --> 00:14:05.079
+involved then I'll usually say yes it
+
+00:14:01.040 --> 00:14:07.920
+does kind so um if it's like uh code to
+
+00:14:05.079 --> 00:14:09.680
+code there that's not code is not
+
+00:14:07.920 --> 00:14:11.480
+natural language it is language but it's
+
+00:14:09.680 --> 00:14:13.000
+not natural language so that might be
+
+00:14:11.480 --> 00:14:15.320
+borderline we might have to discuss
+
+00:14:13.000 --> 00:14:15.320
+about
+
+00:14:15.759 --> 00:14:21.800
+that cool um so next I'd like to talk
+
+00:14:18.880 --> 00:14:25.240
+about methods for creating NLP systems
+
+00:14:21.800 --> 00:14:27.839
+um and there's a lot of different ways
+
+00:14:25.240 --> 00:14:29.720
+to create MLP systems all of these are
+
+00:14:27.839 --> 00:14:32.880
+alive and well in
+
+00:14:29.720 --> 00:14:35.759
+2024 uh the first one is Rule uh
+
+00:14:32.880 --> 00:14:37.959
+rule-based system creation and so the
+
+00:14:35.759 --> 00:14:40.399
+way this works is like let's say you
+
+00:14:37.959 --> 00:14:42.480
+want to build a text classifier you just
+
+00:14:40.399 --> 00:14:46.560
+write the simple python function that
+
+00:14:42.480 --> 00:14:48.639
+classifies things into uh sports or
+
+00:14:46.560 --> 00:14:50.240
+other and the way it classifies it into
+
+00:14:48.639 --> 00:14:52.959
+sports or other is it checks whether
+
+00:14:50.240 --> 00:14:55.160
+baseball soccer football and Tennis are
+
+00:14:52.959 --> 00:14:59.399
+included in the document and classifies
+
+00:14:55.160 --> 00:15:01.959
+it into uh Sports if so uh other if not
+
+00:14:59.399 --> 00:15:05.279
+so has anyone written something like
+
+00:15:01.959 --> 00:15:09.720
+this maybe not a text classifier but um
+
+00:15:05.279 --> 00:15:11.880
+you know to identify entities or uh
+
+00:15:09.720 --> 00:15:14.279
+split words
+
+00:15:11.880 --> 00:15:16.680
+or something like
+
+00:15:14.279 --> 00:15:18.399
+that has anybody not ever written
+
+00:15:16.680 --> 00:15:22.800
+anything like
+
+00:15:18.399 --> 00:15:24.639
+this yeah that's what I thought so um
+
+00:15:22.800 --> 00:15:26.079
+rule-based systems are very convenient
+
+00:15:24.639 --> 00:15:28.920
+when you don't really care about how
+
+00:15:26.079 --> 00:15:30.759
+good your system is um or you're doing
+
+00:15:28.920 --> 00:15:32.360
+that's really really simple and like
+
+00:15:30.759 --> 00:15:35.600
+it'll be perfect even if you do the very
+
+00:15:32.360 --> 00:15:37.079
+simple thing and so I I think it's worth
+
+00:15:35.600 --> 00:15:39.959
+talking a little bit about them and I'll
+
+00:15:37.079 --> 00:15:43.319
+talk a little bit about that uh this
+
+00:15:39.959 --> 00:15:45.680
+time the second thing which like very
+
+00:15:43.319 --> 00:15:47.680
+rapidly over the course of maybe three
+
+00:15:45.680 --> 00:15:50.279
+years or so has become actually maybe
+
+00:15:47.680 --> 00:15:52.720
+the dominant Paradigm in NLP is
+
+00:15:50.279 --> 00:15:56.360
+prompting uh in prompting a language
+
+00:15:52.720 --> 00:15:58.560
+model and the way this works is uh you
+
+00:15:56.360 --> 00:16:00.720
+ask a language model if the following
+
+00:15:58.560 --> 00:16:03.079
+sent is about sports reply Sports
+
+00:16:00.720 --> 00:16:06.120
+otherwise reply other and you feed it to
+
+00:16:03.079 --> 00:16:08.480
+your favorite LM uh usually that's GPT
+
+00:16:06.120 --> 00:16:11.399
+something or other uh sometimes it's an
+
+00:16:08.480 --> 00:16:14.440
+open source model of some variety and
+
+00:16:11.399 --> 00:16:17.759
+then uh it will give you the
+
+00:16:14.440 --> 00:16:20.639
+answer and then finally uh fine-tuning
+
+00:16:17.759 --> 00:16:22.240
+uh so you take some paired data and you
+
+00:16:20.639 --> 00:16:23.600
+do machine learning from paired data
+
+00:16:22.240 --> 00:16:25.680
+where you have something like I love to
+
+00:16:23.600 --> 00:16:27.440
+play baseball uh the stock price is
+
+00:16:25.680 --> 00:16:29.519
+going up he got a hatrick yesterday he
+
+00:16:27.440 --> 00:16:32.759
+is wearing tennis shoes and you assign
+
+00:16:29.519 --> 00:16:35.319
+all these uh labels to them training a
+
+00:16:32.759 --> 00:16:38.160
+model and you can even start out with a
+
+00:16:35.319 --> 00:16:41.480
+prompting based model and fine-tune a a
+
+00:16:38.160 --> 00:16:41.480
+language model
+
+00:16:42.920 --> 00:16:49.399
+also so one major consideration when
+
+00:16:47.519 --> 00:16:52.000
+you're Building Systems like this is the
+
+00:16:49.399 --> 00:16:56.440
+data requirements for building such a
+
+00:16:52.000 --> 00:16:59.319
+system and for rules or prompting where
+
+00:16:56.440 --> 00:17:02.240
+it's just based on intuition really no
+
+00:16:59.319 --> 00:17:04.640
+data is needed whatsoever it you don't
+
+00:17:02.240 --> 00:17:08.240
+need a single example and you can start
+
+00:17:04.640 --> 00:17:11.000
+writing rules or like just just to give
+
+00:17:08.240 --> 00:17:12.640
+an example the rules and prompts I wrote
+
+00:17:11.000 --> 00:17:14.679
+here I didn't look at any examples and I
+
+00:17:12.640 --> 00:17:17.240
+just wrote them uh so this is something
+
+00:17:14.679 --> 00:17:20.000
+that you could start out
+
+00:17:17.240 --> 00:17:21.559
+with uh the problem is you also have no
+
+00:17:20.000 --> 00:17:24.720
+idea how well it works if you don't have
+
+00:17:21.559 --> 00:17:26.760
+any data whatsoever right so um you'll
+
+00:17:24.720 --> 00:17:30.400
+you might be in trouble if you think
+
+00:17:26.760 --> 00:17:30.400
+something should be working
+
+00:17:30.919 --> 00:17:34.440
+so normally the next thing that people
+
+00:17:32.919 --> 00:17:36.880
+move to nowadays when they're building
+
+00:17:34.440 --> 00:17:39.559
+practical systems is rules are prompting
+
+00:17:36.880 --> 00:17:41.240
+based on spot checks so that basically
+
+00:17:39.559 --> 00:17:42.919
+means that you start out with a
+
+00:17:41.240 --> 00:17:45.840
+rule-based system or a prompting based
+
+00:17:42.919 --> 00:17:47.240
+system and then you go in and you run it
+
+00:17:45.840 --> 00:17:48.720
+on some data that you're interested in
+
+00:17:47.240 --> 00:17:50.799
+you just kind of qualitatively look at
+
+00:17:48.720 --> 00:17:52.160
+the data and say oh it's messing up here
+
+00:17:50.799 --> 00:17:53.440
+then you go in and fix your prompt a
+
+00:17:52.160 --> 00:17:54.919
+little bit or you go in and fix your
+
+00:17:53.440 --> 00:17:57.320
+rules a little bit or something like
+
+00:17:54.919 --> 00:18:00.400
+that so uh this is kind of the second
+
+00:17:57.320 --> 00:18:00.400
+level of difficulty
+
+00:18:01.400 --> 00:18:04.640
+so the third level of difficulty would
+
+00:18:03.159 --> 00:18:07.400
+be something like rules are prompting
+
+00:18:04.640 --> 00:18:09.039
+with rigorous evaluation and so here you
+
+00:18:07.400 --> 00:18:12.840
+would create a development set with
+
+00:18:09.039 --> 00:18:14.840
+inputs and outputs uh so you uh create
+
+00:18:12.840 --> 00:18:17.039
+maybe 200 to 2,000
+
+00:18:14.840 --> 00:18:20.080
+examples um
+
+00:18:17.039 --> 00:18:21.720
+and then evaluate your actual accuracy
+
+00:18:20.080 --> 00:18:23.880
+so you need an evaluation metric you
+
+00:18:21.720 --> 00:18:26.120
+need other things like this this is the
+
+00:18:23.880 --> 00:18:28.400
+next level of difficulty but if you're
+
+00:18:26.120 --> 00:18:30.240
+going to be a serious you know NLP
+
+00:18:28.400 --> 00:18:33.000
+engineer or something like this you
+
+00:18:30.240 --> 00:18:34.720
+definitely will be doing this a lot I
+
+00:18:33.000 --> 00:18:37.760
+feel and
+
+00:18:34.720 --> 00:18:40.360
+then so that here now you start needing
+
+00:18:37.760 --> 00:18:41.960
+a depth set and a test set and then
+
+00:18:40.360 --> 00:18:46.280
+finally fine-tuning you need an
+
+00:18:41.960 --> 00:18:48.480
+additional training set um and uh this
+
+00:18:46.280 --> 00:18:52.240
+will generally be a lot bigger than 200
+
+00:18:48.480 --> 00:18:56.080
+to 2,000 examples and generally the rule
+
+00:18:52.240 --> 00:18:56.080
+is that every time you
+
+00:18:57.320 --> 00:19:01.080
+double
+
+00:18:59.520 --> 00:19:02.400
+every time you double your training set
+
+00:19:01.080 --> 00:19:07.480
+size you get about a constant
+
+00:19:02.400 --> 00:19:07.480
+Improvement so if you start
+
+00:19:07.799 --> 00:19:15.080
+out if you start out down here with
+
+00:19:12.240 --> 00:19:17.039
+um zero shot accuracy with a language
+
+00:19:15.080 --> 00:19:21.559
+model you you create a small printing
+
+00:19:17.039 --> 00:19:21.559
+set and you get you know a pretty big
+
+00:19:22.000 --> 00:19:29.120
+increase and then every time you double
+
+00:19:26.320 --> 00:19:30.799
+it it increases by constant fact it's
+
+00:19:29.120 --> 00:19:32.480
+kind of like just in general in machine
+
+00:19:30.799 --> 00:19:37.360
+learning this is a trend that we tend to
+
+00:19:32.480 --> 00:19:40.679
+see so um So based on this
+
+00:19:37.360 --> 00:19:41.880
+uh there's kind of like you get a big
+
+00:19:40.679 --> 00:19:44.200
+gain from having a little bit of
+
+00:19:41.880 --> 00:19:45.760
+training data but the gains very quickly
+
+00:19:44.200 --> 00:19:48.919
+drop off and you start spending a lot of
+
+00:19:45.760 --> 00:19:48.919
+time annotating
+
+00:19:51.000 --> 00:19:55.880
+an so um yeah this is the the general
+
+00:19:54.760 --> 00:19:58.280
+overview of the different types of
+
+00:19:55.880 --> 00:20:00.000
+system building uh any any question
+
+00:19:58.280 --> 00:20:01.559
+questions about this or comments or
+
+00:20:00.000 --> 00:20:04.000
+things like
+
+00:20:01.559 --> 00:20:05.840
+this I think one thing that's changed
+
+00:20:04.000 --> 00:20:08.159
+really drastically from the last time I
+
+00:20:05.840 --> 00:20:09.600
+taught this class is the fact that
+
+00:20:08.159 --> 00:20:11.000
+number one and number two are the things
+
+00:20:09.600 --> 00:20:13.799
+that people are actually doing in
+
+00:20:11.000 --> 00:20:15.360
+practice uh which was you know people
+
+00:20:13.799 --> 00:20:16.679
+who actually care about systems are
+
+00:20:15.360 --> 00:20:18.880
+doing number one and number two is the
+
+00:20:16.679 --> 00:20:20.440
+main thing it used to be that if you
+
+00:20:18.880 --> 00:20:22.679
+were actually serious about building a
+
+00:20:20.440 --> 00:20:24.320
+system uh you really needed to do the
+
+00:20:22.679 --> 00:20:27.080
+funing and now it's kind of like more
+
+00:20:24.320 --> 00:20:27.080
+optional
+
+00:20:27.159 --> 00:20:30.159
+so
+
+00:20:44.039 --> 00:20:50.960
+yeah
+
+00:20:46.320 --> 00:20:53.960
+so it's it's definitely an empirical
+
+00:20:50.960 --> 00:20:53.960
+observation
+
+00:20:54.720 --> 00:21:01.080
+um in terms of the theoretical
+
+00:20:57.640 --> 00:21:03.120
+background I am not I can't immediately
+
+00:21:01.080 --> 00:21:05.840
+point to a
+
+00:21:03.120 --> 00:21:10.039
+particular paper that does that but I
+
+00:21:05.840 --> 00:21:12.720
+think if you think about
+
+00:21:10.039 --> 00:21:14.720
+the I I think I have seen that they do
+
+00:21:12.720 --> 00:21:17.039
+exist in the past but I I can't think of
+
+00:21:14.720 --> 00:21:19.000
+it right now I can try to uh try to come
+
+00:21:17.039 --> 00:21:23.720
+up with an example of
+
+00:21:19.000 --> 00:21:23.720
+that so yeah I I should take
+
+00:21:26.799 --> 00:21:31.960
+notes or someone wants to share one on
+
+00:21:29.360 --> 00:21:33.360
+Piaza uh if you have any ideas and want
+
+00:21:31.960 --> 00:21:34.520
+to share on Patza I'm sure that would be
+
+00:21:33.360 --> 00:21:35.640
+great it'd be great to have a discussion
+
+00:21:34.520 --> 00:21:39.320
+on
+
+00:21:35.640 --> 00:21:44.960
+Patza um Pi
+
+00:21:39.320 --> 00:21:46.880
+one cool okay so next I want to try to
+
+00:21:44.960 --> 00:21:48.200
+make a rule-based system and I'm going
+
+00:21:46.880 --> 00:21:49.360
+to make a rule-based system for
+
+00:21:48.200 --> 00:21:51.799
+sentiment
+
+00:21:49.360 --> 00:21:53.480
+analysis uh and this is a bad idea I
+
+00:21:51.799 --> 00:21:55.400
+would not encourage you to ever do this
+
+00:21:53.480 --> 00:21:57.440
+in real life but I want to do it here to
+
+00:21:55.400 --> 00:21:59.640
+show you why it's a bad idea and like
+
+00:21:57.440 --> 00:22:01.200
+what are some of the hard problems that
+
+00:21:59.640 --> 00:22:03.960
+you encounter when trying to create a
+
+00:22:01.200 --> 00:22:06.600
+system based on rules
+
+00:22:03.960 --> 00:22:08.080
+and then we'll move into building a
+
+00:22:06.600 --> 00:22:12.360
+machine learning base system after we
+
+00:22:08.080 --> 00:22:15.400
+finish this so if we look at the example
+
+00:22:12.360 --> 00:22:18.559
+test this is review sentiment analysis
+
+00:22:15.400 --> 00:22:21.799
+it's one of the most valuable uh tasks
+
+00:22:18.559 --> 00:22:24.039
+uh that people do in NLP nowadays
+
+00:22:21.799 --> 00:22:26.400
+because it allows people to know how
+
+00:22:24.039 --> 00:22:29.200
+customers are thinking about products uh
+
+00:22:26.400 --> 00:22:30.799
+improve their you know their product
+
+00:22:29.200 --> 00:22:32.919
+development and other things like that
+
+00:22:30.799 --> 00:22:34.799
+may monitor people's you know
+
+00:22:32.919 --> 00:22:36.760
+satisfaction with their social media
+
+00:22:34.799 --> 00:22:39.200
+service other things like this so
+
+00:22:36.760 --> 00:22:42.720
+basically the way it works is um you
+
+00:22:39.200 --> 00:22:44.400
+have uh outputs or you have sentences
+
+00:22:42.720 --> 00:22:46.720
+inputs like I hate this movie I love
+
+00:22:44.400 --> 00:22:48.520
+this movie I saw this movie and this
+
+00:22:46.720 --> 00:22:50.600
+gets mapped into positive neutral or
+
+00:22:48.520 --> 00:22:53.120
+negative so I hate this movie would be
+
+00:22:50.600 --> 00:22:55.480
+negative I love this movie positive and
+
+00:22:53.120 --> 00:22:59.039
+I saw this movie is
+
+00:22:55.480 --> 00:23:01.200
+neutral so um
+
+00:22:59.039 --> 00:23:05.200
+that that's the task input tax output
+
+00:23:01.200 --> 00:23:08.880
+labels uh Kary uh sentence
+
+00:23:05.200 --> 00:23:11.679
+label and in order to do this uh we
+
+00:23:08.880 --> 00:23:13.120
+would like to build a model um and we're
+
+00:23:11.679 --> 00:23:16.159
+going to build the model in a rule based
+
+00:23:13.120 --> 00:23:19.000
+way but it we'll still call it a model
+
+00:23:16.159 --> 00:23:21.600
+and the way it works is we do feature
+
+00:23:19.000 --> 00:23:23.159
+extraction um so we extract the Salient
+
+00:23:21.600 --> 00:23:25.279
+features for making the decision about
+
+00:23:23.159 --> 00:23:27.320
+what to Output next we do score
+
+00:23:25.279 --> 00:23:29.880
+calculation calculate a score for one or
+
+00:23:27.320 --> 00:23:32.320
+more possib ities and we have a decision
+
+00:23:29.880 --> 00:23:33.520
+function so we choose one of those
+
+00:23:32.320 --> 00:23:37.679
+several
+
+00:23:33.520 --> 00:23:40.120
+possibilities and so for feature
+
+00:23:37.679 --> 00:23:42.200
+extraction uh formally what this looks
+
+00:23:40.120 --> 00:23:44.240
+like is we have some function and it
+
+00:23:42.200 --> 00:23:48.039
+extracts a feature
+
+00:23:44.240 --> 00:23:51.159
+Vector for score calculation um we
+
+00:23:48.039 --> 00:23:54.240
+calculate the scores based on either a
+
+00:23:51.159 --> 00:23:56.279
+binary classification uh where we have a
+
+00:23:54.240 --> 00:23:58.279
+a weight vector and we take the dot
+
+00:23:56.279 --> 00:24:00.120
+product with our feature vector or we
+
+00:23:58.279 --> 00:24:02.480
+have multi class classification where we
+
+00:24:00.120 --> 00:24:04.520
+have a weight Matrix and we take the
+
+00:24:02.480 --> 00:24:08.640
+product with uh the vector and that
+
+00:24:04.520 --> 00:24:08.640
+gives us you know squares over multiple
+
+00:24:08.919 --> 00:24:14.840
+classes and then we have a decision uh
+
+00:24:11.600 --> 00:24:17.520
+rule so this decision rule tells us what
+
+00:24:14.840 --> 00:24:20.080
+the output is going to be um does anyone
+
+00:24:17.520 --> 00:24:22.200
+know what a typical decision rule is
+
+00:24:20.080 --> 00:24:24.520
+maybe maybe so obvious that you don't
+
+00:24:22.200 --> 00:24:28.760
+think about it often
+
+00:24:24.520 --> 00:24:31.000
+but uh a threshold um so like for would
+
+00:24:28.760 --> 00:24:34.440
+that be for binary a single binary
+
+00:24:31.000 --> 00:24:37.000
+scaler score or a multiple
+
+00:24:34.440 --> 00:24:38.520
+class binary yeah so and then you would
+
+00:24:37.000 --> 00:24:39.960
+pick a threshold and if it's over the
+
+00:24:38.520 --> 00:24:42.919
+threshold
+
+00:24:39.960 --> 00:24:45.760
+you say yes and if it's under the
+
+00:24:42.919 --> 00:24:50.279
+threshold you say no um another option
+
+00:24:45.760 --> 00:24:51.679
+would be um you have a threshold and you
+
+00:24:50.279 --> 00:24:56.080
+say
+
+00:24:51.679 --> 00:24:56.080
+yes no
+
+00:24:56.200 --> 00:25:00.559
+obain so you know you don't give an
+
+00:24:58.360 --> 00:25:02.520
+answer and depending on how you're
+
+00:25:00.559 --> 00:25:03.720
+evaluated what what is a good classifier
+
+00:25:02.520 --> 00:25:07.799
+you might want to abstain some of the
+
+00:25:03.720 --> 00:25:10.960
+time also um for multiclass what what's
+
+00:25:07.799 --> 00:25:10.960
+a standard decision role for
+
+00:25:11.120 --> 00:25:16.720
+multiclass argmax yeah exactly so um
+
+00:25:14.279 --> 00:25:19.520
+basically you you find the index that
+
+00:25:16.720 --> 00:25:22.000
+has the highest score in you output
+
+00:25:19.520 --> 00:25:24.480
+it we're going to be talking about other
+
+00:25:22.000 --> 00:25:26.559
+decision rules also um like
+
+00:25:24.480 --> 00:25:29.480
+self-consistency and minimum based risk
+
+00:25:26.559 --> 00:25:30.760
+later uh for text generation so you can
+
+00:25:29.480 --> 00:25:33.000
+just keep that in mind and then we'll
+
+00:25:30.760 --> 00:25:36.279
+forget about it for like several
+
+00:25:33.000 --> 00:25:39.559
+classes um so for sentiment
+
+00:25:36.279 --> 00:25:42.159
+class um I have a Cod
+
+00:25:39.559 --> 00:25:45.159
+walk
+
+00:25:42.159 --> 00:25:45.159
+here
+
+00:25:46.240 --> 00:25:54.320
+and this is pretty simple um but if
+
+00:25:50.320 --> 00:25:58.559
+you're bored uh of the class and would
+
+00:25:54.320 --> 00:26:01.000
+like to um try out yourself you can
+
+00:25:58.559 --> 00:26:04.480
+Challenge and try to get a better score
+
+00:26:01.000 --> 00:26:06.120
+than I do um over the next few minutes
+
+00:26:04.480 --> 00:26:06.880
+but we have this rule based classifier
+
+00:26:06.120 --> 00:26:10.240
+in
+
+00:26:06.880 --> 00:26:12.640
+here and I will open it up in my vs
+
+00:26:10.240 --> 00:26:15.360
+code
+
+00:26:12.640 --> 00:26:18.360
+to try to create a rule-based classifier
+
+00:26:15.360 --> 00:26:18.360
+and basically the way this
+
+00:26:22.799 --> 00:26:29.960
+works is
+
+00:26:25.159 --> 00:26:29.960
+that we have a feature
+
+00:26:31.720 --> 00:26:37.720
+extraction we have feature extraction we
+
+00:26:34.120 --> 00:26:40.679
+have scoring and we have um a decision
+
+00:26:37.720 --> 00:26:43.480
+rle so here for our feature extraction I
+
+00:26:40.679 --> 00:26:44.720
+have created a list of good words and a
+
+00:26:43.480 --> 00:26:46.720
+list of bad
+
+00:26:44.720 --> 00:26:48.960
+words
+
+00:26:46.720 --> 00:26:51.320
+and what we do is we just count the
+
+00:26:48.960 --> 00:26:53.000
+number of good words that appeared and
+
+00:26:51.320 --> 00:26:55.320
+count the number of bad words that
+
+00:26:53.000 --> 00:26:57.880
+appeared then we also have a bias
+
+00:26:55.320 --> 00:27:01.159
+feature so the bias feature is a feature
+
+00:26:57.880 --> 00:27:03.679
+that's always one and so what that
+
+00:27:01.159 --> 00:27:06.799
+results in is we have a dimension three
+
+00:27:03.679 --> 00:27:08.880
+feature Vector um where this is like the
+
+00:27:06.799 --> 00:27:11.320
+number of good words this is the number
+
+00:27:08.880 --> 00:27:15.320
+of bad words and then you have the
+
+00:27:11.320 --> 00:27:17.760
+bias and then I also Define the feature
+
+00:27:15.320 --> 00:27:20.039
+weights that so for every good word we
+
+00:27:17.760 --> 00:27:22.200
+add one to our score for every bad word
+
+00:27:20.039 --> 00:27:25.559
+we add uh we subtract one from our score
+
+00:27:22.200 --> 00:27:29.399
+and for the BIOS we absor and so we then
+
+00:27:25.559 --> 00:27:30.480
+take the dot product between
+
+00:27:29.399 --> 00:27:34.360
+these
+
+00:27:30.480 --> 00:27:36.919
+two and we get minus
+
+00:27:34.360 --> 00:27:37.640
+0.5 and that gives us uh that gives us
+
+00:27:36.919 --> 00:27:41.000
+the
+
+00:27:37.640 --> 00:27:46.000
+squore so let's run
+
+00:27:41.000 --> 00:27:50.320
+that um and I read in some
+
+00:27:46.000 --> 00:27:52.600
+data and what this data looks like is
+
+00:27:50.320 --> 00:27:55.000
+basically we have a
+
+00:27:52.600 --> 00:27:57.559
+review um which says the rock is
+
+00:27:55.000 --> 00:27:59.480
+destined to be the 21st Century's new
+
+00:27:57.559 --> 00:28:01.240
+Conan and that he's going to make a
+
+00:27:59.480 --> 00:28:03.600
+splash even greater than Arnold
+
+00:28:01.240 --> 00:28:07.000
+Schwarzenegger jeanclaude vanam or
+
+00:28:03.600 --> 00:28:09.519
+Steven Seagal um so this seems pretty
+
+00:28:07.000 --> 00:28:10.840
+positive right I like that's a pretty
+
+00:28:09.519 --> 00:28:13.200
+high order to be better than Arnold
+
+00:28:10.840 --> 00:28:16.080
+Schwarzenegger or John Claude vanam uh
+
+00:28:13.200 --> 00:28:19.519
+if you're familiar with action movies um
+
+00:28:16.080 --> 00:28:22.840
+and so of course this gets a positive
+
+00:28:19.519 --> 00:28:24.120
+label and so uh we have run classifier
+
+00:28:22.840 --> 00:28:25.240
+actually maybe I should call this
+
+00:28:24.120 --> 00:28:27.600
+decision rule because this is
+
+00:28:25.240 --> 00:28:29.120
+essentially our decision Rule and here
+
+00:28:27.600 --> 00:28:32.600
+basically do the thing that I mentioned
+
+00:28:29.120 --> 00:28:35.440
+here the yes no obstain or in this case
+
+00:28:32.600 --> 00:28:38.360
+positive negative neutral so if the
+
+00:28:35.440 --> 00:28:40.159
+score is greater than zero we uh return
+
+00:28:38.360 --> 00:28:42.480
+one if the score is less than zero we
+
+00:28:40.159 --> 00:28:44.679
+return negative one which is negative
+
+00:28:42.480 --> 00:28:47.240
+and otherwise we returns
+
+00:28:44.679 --> 00:28:48.760
+zero um we have an accuracy calculation
+
+00:28:47.240 --> 00:28:51.519
+function just calculating the outputs
+
+00:28:48.760 --> 00:28:55.840
+are good and
+
+00:28:51.519 --> 00:28:57.440
+um this is uh the overall label count in
+
+00:28:55.840 --> 00:28:59.919
+the in the output so we can see there
+
+00:28:57.440 --> 00:29:03.120
+slightly more positives than there are
+
+00:28:59.919 --> 00:29:06.080
+negatives and then we can run this and
+
+00:29:03.120 --> 00:29:10.200
+we get a a score of
+
+00:29:06.080 --> 00:29:14.760
+43 and so one one thing that I have
+
+00:29:10.200 --> 00:29:19.279
+found um is I I do a lot of kind
+
+00:29:14.760 --> 00:29:21.240
+of research on how to make NLP systems
+
+00:29:19.279 --> 00:29:23.600
+better and one of the things I found
+
+00:29:21.240 --> 00:29:26.679
+really invaluable
+
+00:29:23.600 --> 00:29:27.840
+is if you're in a situation where you
+
+00:29:26.679 --> 00:29:29.720
+have a
+
+00:29:27.840 --> 00:29:31.760
+set task and you just want to make the
+
+00:29:29.720 --> 00:29:33.760
+system better on the set task doing
+
+00:29:31.760 --> 00:29:35.159
+comprehensive error analysis and
+
+00:29:33.760 --> 00:29:37.320
+understanding where your system is
+
+00:29:35.159 --> 00:29:39.880
+failing is one of the best ways to do
+
+00:29:37.320 --> 00:29:42.200
+that and I would like to do a very
+
+00:29:39.880 --> 00:29:43.640
+rudimentary version of this here and
+
+00:29:42.200 --> 00:29:46.519
+what I'm doing essentially is I'm just
+
+00:29:43.640 --> 00:29:47.480
+randomly picking uh several examples
+
+00:29:46.519 --> 00:29:49.320
+that were
+
+00:29:47.480 --> 00:29:52.000
+correct
+
+00:29:49.320 --> 00:29:54.840
+um and so like let let's look at the
+
+00:29:52.000 --> 00:29:58.200
+examples here um here the true label is
+
+00:29:54.840 --> 00:30:00.760
+zero um in this predicted one um it may
+
+00:29:58.200 --> 00:30:03.440
+not be as cutting as Woody or as true as
+
+00:30:00.760 --> 00:30:05.039
+back in the Glory Days of uh weekend and
+
+00:30:03.440 --> 00:30:07.440
+two or three things that I know about
+
+00:30:05.039 --> 00:30:09.640
+her but who else engaged in film Mak
+
+00:30:07.440 --> 00:30:12.679
+today is so cognizant of the cultural
+
+00:30:09.640 --> 00:30:14.480
+and moral issues involved in the process
+
+00:30:12.679 --> 00:30:17.600
+so what words in here are a good
+
+00:30:14.480 --> 00:30:20.840
+indication that this is a neutral
+
+00:30:17.600 --> 00:30:20.840
+sentence any
+
+00:30:23.760 --> 00:30:28.399
+ideas little bit tough
+
+00:30:26.240 --> 00:30:30.919
+huh starting to think maybe we should be
+
+00:30:28.399 --> 00:30:30.919
+using machine
+
+00:30:31.480 --> 00:30:37.440
+learning
+
+00:30:34.080 --> 00:30:40.320
+um even by the intentionally low
+
+00:30:37.440 --> 00:30:41.559
+standards of fratboy humor sority boys
+
+00:30:40.320 --> 00:30:43.840
+is a
+
+00:30:41.559 --> 00:30:46.080
+Bowser I think frat boy is maybe
+
+00:30:43.840 --> 00:30:47.360
+negative sentiment if you're familiar
+
+00:30:46.080 --> 00:30:50.360
+with
+
+00:30:47.360 --> 00:30:51.960
+us us I don't have any negative
+
+00:30:50.360 --> 00:30:54.519
+sentiment but the people who say it that
+
+00:30:51.960 --> 00:30:55.960
+way have negative senent maybe so if we
+
+00:30:54.519 --> 00:31:01.080
+wanted to go in and do that we could
+
+00:30:55.960 --> 00:31:01.080
+maybe I won't save this but
+
+00:31:01.519 --> 00:31:08.919
+uh
+
+00:31:04.240 --> 00:31:11.840
+um oh whoops I'll go back and fix it uh
+
+00:31:08.919 --> 00:31:14.840
+crass crass is pretty obviously negative
+
+00:31:11.840 --> 00:31:14.840
+right so I can add
+
+00:31:17.039 --> 00:31:21.080
+crass actually let me just add
+
+00:31:21.760 --> 00:31:29.159
+CR and then um I'll go back and have our
+
+00:31:26.559 --> 00:31:29.159
+train accurate
+
+00:31:32.159 --> 00:31:36.240
+wa maybe maybe I need to run the whole
+
+00:31:33.960 --> 00:31:36.240
+thing
+
+00:31:36.960 --> 00:31:39.960
+again
+
+00:31:40.960 --> 00:31:45.880
+and that budg the training accuracy a
+
+00:31:43.679 --> 00:31:50.360
+little um the dev test accuracy not very
+
+00:31:45.880 --> 00:31:53.919
+much so I could go through and do this
+
+00:31:50.360 --> 00:31:53.919
+um let me add
+
+00:31:54.000 --> 00:31:58.320
+unengaging so I could go through and do
+
+00:31:56.000 --> 00:32:01.720
+this all day and you probably be very
+
+00:31:58.320 --> 00:32:01.720
+bored on
+
+00:32:04.240 --> 00:32:08.360
+engage but I won't do that uh because we
+
+00:32:06.919 --> 00:32:10.679
+have much more important things to be
+
+00:32:08.360 --> 00:32:14.679
+doing
+
+00:32:10.679 --> 00:32:16.440
+um and uh so anyway we um we could go
+
+00:32:14.679 --> 00:32:18.919
+through and design all the features here
+
+00:32:16.440 --> 00:32:21.279
+but like why is this complicated like
+
+00:32:18.919 --> 00:32:22.600
+the the reason why it was complicated
+
+00:32:21.279 --> 00:32:25.840
+became pretty
+
+00:32:22.600 --> 00:32:27.840
+clear from the uh from the very
+
+00:32:25.840 --> 00:32:29.639
+beginning uh the very first example I
+
+00:32:27.840 --> 00:32:32.200
+showed you which was that was a really
+
+00:32:29.639 --> 00:32:34.720
+complicated sentence like all of us
+
+00:32:32.200 --> 00:32:36.240
+could see that it wasn't like really
+
+00:32:34.720 --> 00:32:38.679
+strongly positive it wasn't really
+
+00:32:36.240 --> 00:32:40.519
+strongly negative it was kind of like in
+
+00:32:38.679 --> 00:32:42.919
+the middle but it was in the middle and
+
+00:32:40.519 --> 00:32:44.600
+it said it in a very long way uh you
+
+00:32:42.919 --> 00:32:46.120
+know not using any clearly positive
+
+00:32:44.600 --> 00:32:47.639
+sentiment words not using any clearly
+
+00:32:46.120 --> 00:32:49.760
+negative sentiment
+
+00:32:47.639 --> 00:32:53.760
+words
+
+00:32:49.760 --> 00:32:56.519
+um so yeah basically I I
+
+00:32:53.760 --> 00:33:00.559
+improved um but what are the difficult
+
+00:32:56.519 --> 00:33:03.720
+cases uh that we saw here so the first
+
+00:33:00.559 --> 00:33:07.639
+one is low frequency
+
+00:33:03.720 --> 00:33:09.760
+words so um here's an example the action
+
+00:33:07.639 --> 00:33:11.519
+switches between past and present but
+
+00:33:09.760 --> 00:33:13.120
+the material link is too tenuous to
+
+00:33:11.519 --> 00:33:16.840
+Anchor the emotional connections at
+
+00:33:13.120 --> 00:33:19.519
+purport to span a 125 year divide so
+
+00:33:16.840 --> 00:33:21.080
+this is negative um tenuous is kind of a
+
+00:33:19.519 --> 00:33:22.799
+negative word purport is kind of a
+
+00:33:21.080 --> 00:33:24.760
+negative word but it doesn't appear very
+
+00:33:22.799 --> 00:33:26.159
+frequently so I would need to spend all
+
+00:33:24.760 --> 00:33:29.720
+my time looking for these words and
+
+00:33:26.159 --> 00:33:32.480
+trying to them in um here's yet another
+
+00:33:29.720 --> 00:33:34.240
+horse franchise mucking up its storyline
+
+00:33:32.480 --> 00:33:36.639
+with glitches casual fans could correct
+
+00:33:34.240 --> 00:33:40.159
+in their sleep negative
+
+00:33:36.639 --> 00:33:42.600
+again um so the solutions here are keep
+
+00:33:40.159 --> 00:33:46.880
+working until we get all of them which
+
+00:33:42.600 --> 00:33:49.159
+is maybe not super fun um or incorporate
+
+00:33:46.880 --> 00:33:51.639
+external resources such as sentiment
+
+00:33:49.159 --> 00:33:52.880
+dictionaries that people created uh we
+
+00:33:51.639 --> 00:33:55.960
+could do that but that's a lot of
+
+00:33:52.880 --> 00:33:57.480
+engineering effort to make something
+
+00:33:55.960 --> 00:34:00.639
+work
+
+00:33:57.480 --> 00:34:03.720
+um another one is conjugation so we saw
+
+00:34:00.639 --> 00:34:06.600
+unengaging I guess that's an example of
+
+00:34:03.720 --> 00:34:08.359
+conjugation uh some other ones are
+
+00:34:06.600 --> 00:34:10.520
+operatic sprawling picture that's
+
+00:34:08.359 --> 00:34:12.040
+entertainingly acted magnificently shot
+
+00:34:10.520 --> 00:34:15.480
+and gripping enough to sustain most of
+
+00:34:12.040 --> 00:34:17.399
+its 170 minute length so here we have
+
+00:34:15.480 --> 00:34:19.079
+magnificently so even if I added
+
+00:34:17.399 --> 00:34:20.480
+magnificent this wouldn't have been
+
+00:34:19.079 --> 00:34:23.800
+clocked
+
+00:34:20.480 --> 00:34:26.599
+right um it's basically an overlong
+
+00:34:23.800 --> 00:34:28.839
+episode of tales from the cryp so that's
+
+00:34:26.599 --> 00:34:31.480
+maybe another
+
+00:34:28.839 --> 00:34:33.040
+example um so some things that we could
+
+00:34:31.480 --> 00:34:35.320
+do or what we would have done before the
+
+00:34:33.040 --> 00:34:37.720
+modern Paradigm of machine learning is
+
+00:34:35.320 --> 00:34:40.079
+we would run some sort of normalizer
+
+00:34:37.720 --> 00:34:42.800
+like a stemmer or other things like this
+
+00:34:40.079 --> 00:34:45.240
+in order to convert this into uh the
+
+00:34:42.800 --> 00:34:48.599
+root wordss that we already have seen
+
+00:34:45.240 --> 00:34:52.040
+somewhere in our data or have already
+
+00:34:48.599 --> 00:34:54.040
+handed so that requires um conjugation
+
+00:34:52.040 --> 00:34:55.879
+analysis or morphological analysis as we
+
+00:34:54.040 --> 00:34:57.400
+say it in
+
+00:34:55.879 --> 00:35:00.680
+technicals
+
+00:34:57.400 --> 00:35:03.960
+negation this is a tricky one so this
+
+00:35:00.680 --> 00:35:06.760
+one's not nearly as Dreadful as expected
+
+00:35:03.960 --> 00:35:08.800
+so Dreadful is a pretty bad word right
+
+00:35:06.760 --> 00:35:13.000
+but not nearly as Dreadful as expected
+
+00:35:08.800 --> 00:35:14.440
+is like a solidly neutral um you know or
+
+00:35:13.000 --> 00:35:16.359
+maybe even
+
+00:35:14.440 --> 00:35:18.920
+positive I would I would say that's
+
+00:35:16.359 --> 00:35:20.640
+neutral but you know uh neutral or
+
+00:35:18.920 --> 00:35:23.800
+positive it's definitely not
+
+00:35:20.640 --> 00:35:26.359
+negative um serving s doesn't serve up a
+
+00:35:23.800 --> 00:35:29.480
+whole lot of laughs so laughs is
+
+00:35:26.359 --> 00:35:31.880
+obviously positive but not serving UPS
+
+00:35:29.480 --> 00:35:34.440
+is obviously
+
+00:35:31.880 --> 00:35:36.839
+negative so if negation modifies the
+
+00:35:34.440 --> 00:35:38.240
+word disregard it now we would probably
+
+00:35:36.839 --> 00:35:41.440
+need to do some sort of syntactic
+
+00:35:38.240 --> 00:35:45.599
+analysis or semantic analysis of
+
+00:35:41.440 --> 00:35:47.520
+some metaphor an analogy so puts a human
+
+00:35:45.599 --> 00:35:50.640
+face on a land most westerners are
+
+00:35:47.520 --> 00:35:52.880
+unfamiliar though uh this is
+
+00:35:50.640 --> 00:35:54.960
+positive green might want to hang on to
+
+00:35:52.880 --> 00:35:58.800
+that ski mask as robbery may be the only
+
+00:35:54.960 --> 00:35:58.800
+way to pay for this next project
+
+00:35:58.839 --> 00:36:03.640
+so this this is saying that the movie
+
+00:36:01.960 --> 00:36:05.560
+was so bad that the director will have
+
+00:36:03.640 --> 00:36:08.359
+to rob people in order to get money for
+
+00:36:05.560 --> 00:36:11.000
+the next project so that's kind of bad I
+
+00:36:08.359 --> 00:36:12.880
+guess um has all the depth of a waiting
+
+00:36:11.000 --> 00:36:14.520
+pool this is kind of my favorite one
+
+00:36:12.880 --> 00:36:15.880
+because it's really short and sweet but
+
+00:36:14.520 --> 00:36:18.800
+you know you need to know how deep a
+
+00:36:15.880 --> 00:36:21.440
+waiting pool is um so that's
+
+00:36:18.800 --> 00:36:22.960
+negative so the solution here I don't
+
+00:36:21.440 --> 00:36:24.680
+really even know how to handle this with
+
+00:36:22.960 --> 00:36:26.880
+a rule based system I have no idea how
+
+00:36:24.680 --> 00:36:30.040
+we would possibly do this yeah machine
+
+00:36:26.880 --> 00:36:32.400
+learning based models seem to be pretty
+
+00:36:30.040 --> 00:36:37.000
+adaptive okay and then I start doing
+
+00:36:32.400 --> 00:36:37.000
+these ones um anyone have a good
+
+00:36:38.160 --> 00:36:46.800
+idea any any other friends who know
+
+00:36:42.520 --> 00:36:50.040
+Japanese no okay um so yeah that's
+
+00:36:46.800 --> 00:36:52.839
+positive um that one's negative uh and
+
+00:36:50.040 --> 00:36:54.920
+the solution here is learn Japanese I
+
+00:36:52.839 --> 00:36:56.800
+guess or whatever other language you
+
+00:36:54.920 --> 00:37:00.040
+want to process so like obviously
+
+00:36:56.800 --> 00:37:03.720
+rule-based systems don't scale very
+
+00:37:00.040 --> 00:37:05.119
+well so um we've moved but like rule
+
+00:37:03.720 --> 00:37:06.319
+based systems don't scale very well
+
+00:37:05.119 --> 00:37:08.160
+we're not going to be using them for
+
+00:37:06.319 --> 00:37:11.400
+most of the things we do in this class
+
+00:37:08.160 --> 00:37:14.240
+but I do think it's sometimes useful to
+
+00:37:11.400 --> 00:37:15.640
+try to create one for your task maybe
+
+00:37:14.240 --> 00:37:16.680
+right at the very beginning of a project
+
+00:37:15.640 --> 00:37:18.560
+because it gives you an idea about
+
+00:37:16.680 --> 00:37:21.160
+what's really hard about the task in
+
+00:37:18.560 --> 00:37:22.480
+some cases so um yeah I wouldn't
+
+00:37:21.160 --> 00:37:25.599
+entirely discount them I'm not
+
+00:37:22.480 --> 00:37:27.400
+introducing them for no reason
+
+00:37:25.599 --> 00:37:29.880
+whatsoever
+
+00:37:27.400 --> 00:37:34.160
+so next is machine learning based anal
+
+00:37:29.880 --> 00:37:35.400
+and machine learning uh in general uh I
+
+00:37:34.160 --> 00:37:36.640
+here actually when I say machine
+
+00:37:35.400 --> 00:37:38.160
+learning I'm going to be talking about
+
+00:37:36.640 --> 00:37:39.560
+the traditional fine-tuning approach
+
+00:37:38.160 --> 00:37:43.520
+where we have a training set Dev set
+
+00:37:39.560 --> 00:37:46.359
+test set and so we take our training set
+
+00:37:43.520 --> 00:37:49.680
+we run some learning algorithm over it
+
+00:37:46.359 --> 00:37:52.319
+we have a learned feature extractor F A
+
+00:37:49.680 --> 00:37:55.839
+possibly learned feature extractor F
+
+00:37:52.319 --> 00:37:57.880
+possibly learned scoring function W and
+
+00:37:55.839 --> 00:38:00.800
+uh then we apply our inference algorithm
+
+00:37:57.880 --> 00:38:02.839
+our decision Rule and make decisions
+
+00:38:00.800 --> 00:38:04.200
+when I say possibly learned actually the
+
+00:38:02.839 --> 00:38:06.119
+first example I'm going to give of a
+
+00:38:04.200 --> 00:38:07.760
+machine learning based technique is uh
+
+00:38:06.119 --> 00:38:10.079
+doesn't have a learned feature extractor
+
+00:38:07.760 --> 00:38:12.800
+but most things that we use nowadays do
+
+00:38:10.079 --> 00:38:12.800
+have learned feature
+
+00:38:13.200 --> 00:38:18.040
+extractors so our first attempt is going
+
+00:38:15.640 --> 00:38:21.760
+to be a bag of words model uh and the
+
+00:38:18.040 --> 00:38:27.119
+way a bag of wordss model works is uh
+
+00:38:21.760 --> 00:38:30.160
+essentially we start out by looking up a
+
+00:38:27.119 --> 00:38:33.240
+Vector where one element in the vector
+
+00:38:30.160 --> 00:38:36.240
+is uh is one and all the other elements
+
+00:38:33.240 --> 00:38:38.040
+in the vector are zero and so if the
+
+00:38:36.240 --> 00:38:40.319
+word is different the position in the
+
+00:38:38.040 --> 00:38:42.839
+vector that's one will be different we
+
+00:38:40.319 --> 00:38:46.280
+add all of these together and this gives
+
+00:38:42.839 --> 00:38:48.200
+us a vector where each element is the
+
+00:38:46.280 --> 00:38:50.359
+frequency of that word in the vector and
+
+00:38:48.200 --> 00:38:52.520
+then we multiply that by weights and we
+
+00:38:50.359 --> 00:38:55.520
+get a
+
+00:38:52.520 --> 00:38:57.160
+score and um here as I said this is not
+
+00:38:55.520 --> 00:39:00.359
+a learned feature
+
+00:38:57.160 --> 00:39:02.079
+uh Vector this is basically uh sorry not
+
+00:39:00.359 --> 00:39:04.359
+a learn feature extractor this is
+
+00:39:02.079 --> 00:39:06.200
+basically a fixed feature extractor but
+
+00:39:04.359 --> 00:39:09.839
+the weights themselves are
+
+00:39:06.200 --> 00:39:11.640
+learned um so my my question is I
+
+00:39:09.839 --> 00:39:14.599
+mentioned a whole lot of problems before
+
+00:39:11.640 --> 00:39:17.480
+I mentioned infrequent words I mentioned
+
+00:39:14.599 --> 00:39:20.760
+conjugation I mentioned uh different
+
+00:39:17.480 --> 00:39:22.880
+languages I mentioned syntax and
+
+00:39:20.760 --> 00:39:24.599
+metaphor so which of these do we think
+
+00:39:22.880 --> 00:39:25.440
+would be fixed by this sort of learning
+
+00:39:24.599 --> 00:39:27.400
+based
+
+00:39:25.440 --> 00:39:29.640
+approach
+
+00:39:27.400 --> 00:39:29.640
+any
+
+00:39:29.920 --> 00:39:35.200
+ideas maybe not fixed maybe made
+
+00:39:32.520 --> 00:39:35.200
+significantly
+
+00:39:36.880 --> 00:39:41.560
+better any Brave uh brave
+
+00:39:44.880 --> 00:39:48.440
+people maybe maybe
+
+00:39:53.720 --> 00:39:58.400
+negation okay so maybe doesn't when it
+
+00:39:55.760 --> 00:39:58.400
+have a negative qu
+
+00:40:02.960 --> 00:40:07.560
+yeah yeah so for the conjugation if we
+
+00:40:05.520 --> 00:40:09.200
+had the conjugations of the stems mapped
+
+00:40:07.560 --> 00:40:11.119
+in the same position that might fix a
+
+00:40:09.200 --> 00:40:12.920
+conjugation problem but I would say if
+
+00:40:11.119 --> 00:40:15.200
+you don't do that then this kind of
+
+00:40:12.920 --> 00:40:18.160
+fixes conjugation a little bit but maybe
+
+00:40:15.200 --> 00:40:21.319
+not not really yeah kind of fix
+
+00:40:18.160 --> 00:40:24.079
+conjugation because like they're using
+
+00:40:21.319 --> 00:40:26.760
+the same there
+
+00:40:24.079 --> 00:40:28.400
+probably different variations so we
+
+00:40:26.760 --> 00:40:31.359
+learn how to
+
+00:40:28.400 --> 00:40:33.400
+classify surrounding
+
+00:40:31.359 --> 00:40:35.000
+structure yeah if it's a big enough
+
+00:40:33.400 --> 00:40:36.760
+training set you might have covered the
+
+00:40:35.000 --> 00:40:37.880
+various conjugations but if you haven't
+
+00:40:36.760 --> 00:40:43.000
+and you don't have any rule-based
+
+00:40:37.880 --> 00:40:43.000
+processing it it might still be problems
+
+00:40:45.400 --> 00:40:50.359
+yeah yeah so in frequent words if you
+
+00:40:48.280 --> 00:40:52.560
+have a large enough training set yeah
+
+00:40:50.359 --> 00:40:54.599
+you'll be able to fix it to some extent
+
+00:40:52.560 --> 00:40:56.480
+so none of the problems are entirely
+
+00:40:54.599 --> 00:40:57.880
+fixed but a lot of them are made better
+
+00:40:56.480 --> 00:40:58.960
+different languages is also made better
+
+00:40:57.880 --> 00:41:00.119
+if you have training data in that
+
+00:40:58.960 --> 00:41:04.599
+language but if you don't then you're
+
+00:41:00.119 --> 00:41:06.240
+out of BL so um so now what I'd like to
+
+00:41:04.599 --> 00:41:10.800
+do is I'd look to like to look at what
+
+00:41:06.240 --> 00:41:15.079
+our vectors represent so basically um in
+
+00:41:10.800 --> 00:41:16.880
+uh in binary classification each word um
+
+00:41:15.079 --> 00:41:19.119
+sorry so the vectors themselves
+
+00:41:16.880 --> 00:41:21.880
+represent the counts of the words here
+
+00:41:19.119 --> 00:41:25.319
+I'm talking about what the weight uh
+
+00:41:21.880 --> 00:41:28.520
+vectors or matrices correspond to and
+
+00:41:25.319 --> 00:41:31.640
+the weight uh Vector here will be
+
+00:41:28.520 --> 00:41:33.680
+positive if the word it tends to be
+
+00:41:31.640 --> 00:41:36.680
+positive if in a binary classification
+
+00:41:33.680 --> 00:41:38.400
+case in a multiclass classification case
+
+00:41:36.680 --> 00:41:42.480
+we'll actually have a matrix that looks
+
+00:41:38.400 --> 00:41:45.480
+like this where um each column or row uh
+
+00:41:42.480 --> 00:41:47.079
+corresponds to the word and each row or
+
+00:41:45.480 --> 00:41:49.319
+column corresponds to a label and it
+
+00:41:47.079 --> 00:41:51.960
+will be higher if that row tends to uh
+
+00:41:49.319 --> 00:41:54.800
+correlate with that uh that word tends
+
+00:41:51.960 --> 00:41:56.920
+to correlate that little
+
+00:41:54.800 --> 00:41:59.240
+bit so
+
+00:41:56.920 --> 00:42:04.079
+this um training of the bag of words
+
+00:41:59.240 --> 00:42:07.720
+model is can be done uh so simply that
+
+00:42:04.079 --> 00:42:10.200
+we uh can put it in a single slide so
+
+00:42:07.720 --> 00:42:11.599
+basically here uh what we do is we start
+
+00:42:10.200 --> 00:42:14.760
+out with the feature
+
+00:42:11.599 --> 00:42:18.880
+weights and for each example in our data
+
+00:42:14.760 --> 00:42:20.800
+set we extract features um the exact way
+
+00:42:18.880 --> 00:42:23.920
+I'm extracting features is basically
+
+00:42:20.800 --> 00:42:25.720
+splitting uh splitting the words using
+
+00:42:23.920 --> 00:42:28.000
+the python split function and then uh
+
+00:42:25.720 --> 00:42:31.319
+Counting number of times each word
+
+00:42:28.000 --> 00:42:33.160
+exists uh we then run the classifier so
+
+00:42:31.319 --> 00:42:36.280
+actually running the classifier is
+
+00:42:33.160 --> 00:42:38.200
+exactly the same as what we did for the
+
+00:42:36.280 --> 00:42:42.640
+uh the rule based system it's just that
+
+00:42:38.200 --> 00:42:47.359
+we have feature vectors instead and
+
+00:42:42.640 --> 00:42:51.559
+then if the predicted value is
+
+00:42:47.359 --> 00:42:55.160
+not value then for each of the
+
+00:42:51.559 --> 00:42:56.680
+features uh in the feature space we
+
+00:42:55.160 --> 00:43:02.200
+upweight
+
+00:42:56.680 --> 00:43:03.599
+the um we upweight The Weight by the
+
+00:43:02.200 --> 00:43:06.000
+vector
+
+00:43:03.599 --> 00:43:09.920
+size by or by the amount of the vector
+
+00:43:06.000 --> 00:43:13.240
+if Y is positive and we downweight the
+
+00:43:09.920 --> 00:43:16.240
+vector uh by the size of the vector if Y
+
+00:43:13.240 --> 00:43:18.520
+is negative so this is really really
+
+00:43:16.240 --> 00:43:20.559
+simple it's uh probably the simplest
+
+00:43:18.520 --> 00:43:25.079
+possible algorithm for training one of
+
+00:43:20.559 --> 00:43:27.559
+these models um but I have an
+
+00:43:25.079 --> 00:43:30.040
+example in this that you can also take a
+
+00:43:27.559 --> 00:43:31.960
+look at here's a trained bag of words
+
+00:43:30.040 --> 00:43:33.680
+classifier and we could step through
+
+00:43:31.960 --> 00:43:34.960
+this is on exactly the same data set as
+
+00:43:33.680 --> 00:43:37.240
+I did before we're training on the
+
+00:43:34.960 --> 00:43:42.359
+training set
+
+00:43:37.240 --> 00:43:43.640
+um and uh evaluating on the dev set um I
+
+00:43:42.359 --> 00:43:45.880
+also have some extra stuff like I'm
+
+00:43:43.640 --> 00:43:47.079
+Shuffling the order of the data IDs
+
+00:43:45.880 --> 00:43:49.440
+which is really important if you're
+
+00:43:47.079 --> 00:43:53.160
+doing this sort of incremental algorithm
+
+00:43:49.440 --> 00:43:54.960
+uh because uh what if what if your
+
+00:43:53.160 --> 00:43:57.400
+creating data set was ordered in this
+
+00:43:54.960 --> 00:44:00.040
+way where you have all of the positive
+
+00:43:57.400 --> 00:44:00.040
+labels on
+
+00:44:00.359 --> 00:44:04.520
+top and then you have all of the
+
+00:44:02.280 --> 00:44:06.680
+negative labels on the
+
+00:44:04.520 --> 00:44:08.200
+bottom if you do something like this it
+
+00:44:06.680 --> 00:44:10.200
+would see only negative labels at the
+
+00:44:08.200 --> 00:44:11.800
+end of training and you might have
+
+00:44:10.200 --> 00:44:14.400
+problems because your model would only
+
+00:44:11.800 --> 00:44:17.440
+predict negatives so we also Shuffle
+
+00:44:14.400 --> 00:44:20.319
+data um and then step through we run the
+
+00:44:17.440 --> 00:44:22.559
+classifier and I'm going to run uh five
+
+00:44:20.319 --> 00:44:23.640
+epochs of training through the data set
+
+00:44:22.559 --> 00:44:27.160
+uh very
+
+00:44:23.640 --> 00:44:29.599
+fast and calculate our accuracy
+
+00:44:27.160 --> 00:44:33.280
+and this got 75% accuracy on the
+
+00:44:29.599 --> 00:44:36.160
+training data set and uh 56% accuracy on
+
+00:44:33.280 --> 00:44:40.000
+the Deb data set so uh if you remember
+
+00:44:36.160 --> 00:44:41.520
+our rule-based classifier had 42 uh 42
+
+00:44:40.000 --> 00:44:43.880
+accuracy and now our training based
+
+00:44:41.520 --> 00:44:45.760
+classifier has 56 accuracy but it's
+
+00:44:43.880 --> 00:44:49.359
+overfitting heavily to the training side
+
+00:44:45.760 --> 00:44:50.880
+so um basically this is a pretty strong
+
+00:44:49.359 --> 00:44:53.480
+advertisement for why we should be using
+
+00:44:50.880 --> 00:44:54.960
+machine learning you know I the amount
+
+00:44:53.480 --> 00:44:57.800
+of code that we had for this machine
+
+00:44:54.960 --> 00:44:59.720
+learning model is basically very similar
+
+00:44:57.800 --> 00:45:02.680
+um it's not using any external libraries
+
+00:44:59.720 --> 00:45:02.680
+but we're getting better at
+
+00:45:03.599 --> 00:45:08.800
+this
+
+00:45:05.800 --> 00:45:08.800
+cool
+
+00:45:09.559 --> 00:45:16.000
+so cool any any questions
+
+00:45:13.520 --> 00:45:18.240
+here and so I'm going to talk about the
+
+00:45:16.000 --> 00:45:20.760
+connection to between this algorithm and
+
+00:45:18.240 --> 00:45:22.839
+neural networks in the next class um
+
+00:45:20.760 --> 00:45:24.200
+because this actually is using a very
+
+00:45:22.839 --> 00:45:26.319
+similar training algorithm to what we
+
+00:45:24.200 --> 00:45:27.480
+use in neural networks with some uh
+
+00:45:26.319 --> 00:45:30.079
+particular
+
+00:45:27.480 --> 00:45:32.839
+assumptions cool um so what's missing in
+
+00:45:30.079 --> 00:45:34.800
+bag of words um still handling of
+
+00:45:32.839 --> 00:45:36.880
+conjugation or compound words is not
+
+00:45:34.800 --> 00:45:39.160
+perfect it we can do it to some extent
+
+00:45:36.880 --> 00:45:41.079
+to the point where we can uh memorize
+
+00:45:39.160 --> 00:45:44.079
+things so I love this movie I love this
+
+00:45:41.079 --> 00:45:46.920
+movie another thing is handling word Ser
+
+00:45:44.079 --> 00:45:49.240
+uh similarities so I love this movie and
+
+00:45:46.920 --> 00:45:50.720
+I adore this movie uh these basically
+
+00:45:49.240 --> 00:45:52.119
+mean the same thing as humans we know
+
+00:45:50.720 --> 00:45:54.200
+they mean the same thing so we should be
+
+00:45:52.119 --> 00:45:56.079
+able to take advantage of that fact to
+
+00:45:54.200 --> 00:45:57.839
+learn better models but we're not doing
+
+00:45:56.079 --> 00:46:02.760
+that in this model at the moment because
+
+00:45:57.839 --> 00:46:05.440
+each unit is uh treated as a atomic unit
+
+00:46:02.760 --> 00:46:08.040
+and there's no idea of
+
+00:46:05.440 --> 00:46:11.040
+similarity also handling of combination
+
+00:46:08.040 --> 00:46:12.760
+features so um I love this movie and I
+
+00:46:11.040 --> 00:46:14.920
+don't love this movie I hate this movie
+
+00:46:12.760 --> 00:46:17.079
+and I don't hate this movie actually
+
+00:46:14.920 --> 00:46:20.400
+this is a little bit tricky because
+
+00:46:17.079 --> 00:46:23.240
+negative words are slightly indicative
+
+00:46:20.400 --> 00:46:25.280
+of it being negative but actually what
+
+00:46:23.240 --> 00:46:28.119
+they do is they negate the other things
+
+00:46:25.280 --> 00:46:28.119
+that you're saying in the
+
+00:46:28.240 --> 00:46:36.559
+sentence
+
+00:46:30.720 --> 00:46:40.480
+so um like love is positive hate is
+
+00:46:36.559 --> 00:46:40.480
+negative but like don't
+
+00:46:50.359 --> 00:46:56.079
+love it's actually kind of like this
+
+00:46:52.839 --> 00:46:59.359
+right like um Love is very positive POS
+
+00:46:56.079 --> 00:47:01.760
+hate is very negative but don't love is
+
+00:46:59.359 --> 00:47:04.680
+like slightly less positive than don't
+
+00:47:01.760 --> 00:47:06.160
+hate right so um It's actually kind of
+
+00:47:04.680 --> 00:47:07.559
+tricky because you need to combine them
+
+00:47:06.160 --> 00:47:10.720
+together and figure out what's going on
+
+00:47:07.559 --> 00:47:12.280
+based on that another example that a lot
+
+00:47:10.720 --> 00:47:14.160
+of people might not think of immediately
+
+00:47:12.280 --> 00:47:17.880
+but is super super common in sentiment
+
+00:47:14.160 --> 00:47:20.160
+analysis or any other thing is butt so
+
+00:47:17.880 --> 00:47:22.599
+basically what but does is it throws
+
+00:47:20.160 --> 00:47:24.160
+away all the stuff that you said before
+
+00:47:22.599 --> 00:47:26.119
+um and you can just pay attention to the
+
+00:47:24.160 --> 00:47:29.000
+stuff that you saw beforehand so like we
+
+00:47:26.119 --> 00:47:30.440
+could even add this to our um like if
+
+00:47:29.000 --> 00:47:31.760
+you want to add this to your rule based
+
+00:47:30.440 --> 00:47:33.240
+classifier you can do that you just
+
+00:47:31.760 --> 00:47:34.640
+search for butt and delete everything
+
+00:47:33.240 --> 00:47:37.240
+before it and see if that inputs your
+
+00:47:34.640 --> 00:47:39.240
+accuracy might be might be a fun very
+
+00:47:37.240 --> 00:47:43.480
+quick thing
+
+00:47:39.240 --> 00:47:44.880
+to cool so the better solution which is
+
+00:47:43.480 --> 00:47:46.800
+what we're going to talk about for every
+
+00:47:44.880 --> 00:47:49.480
+other class other than uh other than
+
+00:47:46.800 --> 00:47:52.160
+this one is neural network models and
+
+00:47:49.480 --> 00:47:55.800
+basically uh what they do is they do a
+
+00:47:52.160 --> 00:47:59.400
+lookup of uh dense word embeddings so
+
+00:47:55.800 --> 00:48:02.520
+instead of looking up uh individual uh
+
+00:47:59.400 --> 00:48:04.640
+sparse uh vectors individual one hot
+
+00:48:02.520 --> 00:48:06.920
+vectors they look up dense word
+
+00:48:04.640 --> 00:48:09.680
+embeddings and then throw them into some
+
+00:48:06.920 --> 00:48:11.880
+complicated function to extract features
+
+00:48:09.680 --> 00:48:16.359
+and based on the features uh multiply by
+
+00:48:11.880 --> 00:48:18.280
+weights and get a score um and if you're
+
+00:48:16.359 --> 00:48:20.359
+doing text classification in the
+
+00:48:18.280 --> 00:48:22.520
+traditional way this is normally what
+
+00:48:20.359 --> 00:48:23.760
+you do um if you're doing text
+
+00:48:22.520 --> 00:48:25.960
+classification with something like
+
+00:48:23.760 --> 00:48:27.280
+prompting you're still actually doing
+
+00:48:25.960 --> 00:48:29.960
+this because you're calculating the
+
+00:48:27.280 --> 00:48:32.960
+score of the next word to predict and
+
+00:48:29.960 --> 00:48:34.720
+that's done in exactly the same way so
+
+00:48:32.960 --> 00:48:37.760
+uh even if you're using a large language
+
+00:48:34.720 --> 00:48:39.359
+model like GPT this is still probably
+
+00:48:37.760 --> 00:48:41.800
+happening under the hood unless open the
+
+00:48:39.359 --> 00:48:43.400
+eye invented something that very
+
+00:48:41.800 --> 00:48:45.559
+different in Alien than anything else
+
+00:48:43.400 --> 00:48:48.440
+that we know of but I I'm guessing that
+
+00:48:45.559 --> 00:48:48.440
+that propably hasn't
+
+00:48:48.480 --> 00:48:52.880
+happen um one nice thing about neural
+
+00:48:50.880 --> 00:48:54.480
+networks is neural networks
+
+00:48:52.880 --> 00:48:57.559
+theoretically are powerful enough to
+
+00:48:54.480 --> 00:49:00.000
+solve any task if you make them uh deep
+
+00:48:57.559 --> 00:49:01.160
+enough or wide enough uh like if you
+
+00:49:00.000 --> 00:49:04.520
+make them wide enough and then if you
+
+00:49:01.160 --> 00:49:06.799
+make them deep it also helps further so
+
+00:49:04.520 --> 00:49:08.079
+anytime somebody says well you can't
+
+00:49:06.799 --> 00:49:11.119
+just solve that problem with neural
+
+00:49:08.079 --> 00:49:13.240
+networks you know that they're lying
+
+00:49:11.119 --> 00:49:15.720
+basically because they theoretically can
+
+00:49:13.240 --> 00:49:17.359
+solve every problem uh but you have you
+
+00:49:15.720 --> 00:49:19.799
+have issues of data you have issues of
+
+00:49:17.359 --> 00:49:23.079
+other things like that so you know they
+
+00:49:19.799 --> 00:49:23.079
+don't just necessarily work
+
+00:49:23.119 --> 00:49:28.040
+outs cool um so the final thing I'd like
+
+00:49:26.400 --> 00:49:29.319
+to talk about is the road map going
+
+00:49:28.040 --> 00:49:31.319
+forward some of the things I'm going to
+
+00:49:29.319 --> 00:49:32.799
+cover in the class and some of the
+
+00:49:31.319 --> 00:49:35.200
+logistics
+
+00:49:32.799 --> 00:49:36.799
+issues so um the first thing I'm going
+
+00:49:35.200 --> 00:49:38.240
+to talk about in the class is language
+
+00:49:36.799 --> 00:49:40.559
+modeling fun
+
+00:49:38.240 --> 00:49:42.720
+fundamentals and uh so this could
+
+00:49:40.559 --> 00:49:44.240
+include language models uh that just
+
+00:49:42.720 --> 00:49:46.559
+predict the next words it could include
+
+00:49:44.240 --> 00:49:50.559
+language models that predict the output
+
+00:49:46.559 --> 00:49:51.599
+given the uh the input or the prompt um
+
+00:49:50.559 --> 00:49:54.559
+I'm going to be talking about
+
+00:49:51.599 --> 00:49:56.520
+representing words uh how how we get
+
+00:49:54.559 --> 00:49:59.319
+word representation subword models other
+
+00:49:56.520 --> 00:50:01.440
+things like that uh then go kind of
+
+00:49:59.319 --> 00:50:04.200
+deeper into language modeling uh how do
+
+00:50:01.440 --> 00:50:07.799
+we do it how do we evaluate it other
+
+00:50:04.200 --> 00:50:10.920
+things um sequence encoding uh and this
+
+00:50:07.799 --> 00:50:13.240
+is going to cover things like uh
+
+00:50:10.920 --> 00:50:16.280
+Transformers uh self attention modals
+
+00:50:13.240 --> 00:50:18.559
+but also very quickly cnns and rnns
+
+00:50:16.280 --> 00:50:20.880
+which are useful in some
+
+00:50:18.559 --> 00:50:22.200
+cases um and then we're going to
+
+00:50:20.880 --> 00:50:24.040
+specifically go very deep into the
+
+00:50:22.200 --> 00:50:25.960
+Transformer architecture and also talk a
+
+00:50:24.040 --> 00:50:27.280
+little bit about some of the modern uh
+
+00:50:25.960 --> 00:50:30.240
+improvements to the Transformer
+
+00:50:27.280 --> 00:50:31.839
+architecture so the Transformer we're
+
+00:50:30.240 --> 00:50:33.839
+using nowadays is very different than
+
+00:50:31.839 --> 00:50:36.200
+the Transformer that was invented in
+
+00:50:33.839 --> 00:50:37.240
+2017 uh so we're going to talk well I
+
+00:50:36.200 --> 00:50:38.760
+wouldn't say very different but
+
+00:50:37.240 --> 00:50:41.359
+different enough that it's important so
+
+00:50:38.760 --> 00:50:43.280
+we're going to talk about some of those
+
+00:50:41.359 --> 00:50:45.079
+things second thing I'd like to talk
+
+00:50:43.280 --> 00:50:47.000
+about is training and inference methods
+
+00:50:45.079 --> 00:50:48.839
+so this includes uh generation
+
+00:50:47.000 --> 00:50:52.119
+algorithms uh so we're going to have a
+
+00:50:48.839 --> 00:50:55.520
+whole class on how we generate text uh
+
+00:50:52.119 --> 00:50:58.319
+in different ways uh prompting how uh we
+
+00:50:55.520 --> 00:50:59.720
+can prompt things I hear uh world class
+
+00:50:58.319 --> 00:51:01.799
+prompt engineers make a lot of money
+
+00:50:59.720 --> 00:51:05.480
+nowadays so uh you'll want to pay
+
+00:51:01.799 --> 00:51:08.760
+attention to that one um and instruction
+
+00:51:05.480 --> 00:51:11.520
+tuning uh so how do we train models to
+
+00:51:08.760 --> 00:51:13.720
+handle a lot of different tasks and
+
+00:51:11.520 --> 00:51:15.839
+reinforcement learning so how do we uh
+
+00:51:13.720 --> 00:51:18.520
+you know like actually generate outputs
+
+00:51:15.839 --> 00:51:19.839
+uh kind of Judge them and then learn
+
+00:51:18.520 --> 00:51:22.599
+from
+
+00:51:19.839 --> 00:51:25.880
+there also experimental design and
+
+00:51:22.599 --> 00:51:28.079
+evaluation so experimental design uh so
+
+00:51:25.880 --> 00:51:30.480
+how do we design an experiment well uh
+
+00:51:28.079 --> 00:51:32.000
+so that it backs up what we want to be
+
+00:51:30.480 --> 00:51:34.559
+uh our conclusions that we want to be
+
+00:51:32.000 --> 00:51:37.000
+backing up how do we do human annotation
+
+00:51:34.559 --> 00:51:38.880
+of data in a reliable way this is
+
+00:51:37.000 --> 00:51:41.160
+getting harder and harder as models get
+
+00:51:38.880 --> 00:51:43.359
+better and better because uh getting
+
+00:51:41.160 --> 00:51:45.000
+humans who don't care very much about
+
+00:51:43.359 --> 00:51:48.559
+The annotation task they might do worse
+
+00:51:45.000 --> 00:51:51.119
+than gp4 so um you need to be careful of
+
+00:51:48.559 --> 00:51:52.240
+that also debugging and interpretation
+
+00:51:51.119 --> 00:51:53.960
+technique so what are some of the
+
+00:51:52.240 --> 00:51:55.160
+automatic techniques that you can do to
+
+00:51:53.960 --> 00:51:57.720
+quickly figure out what's going wrong
+
+00:51:55.160 --> 00:52:00.040
+with your models and improve
+
+00:51:57.720 --> 00:52:01.599
+them and uh bias and fairness
+
+00:52:00.040 --> 00:52:04.200
+considerations so it's really really
+
+00:52:01.599 --> 00:52:05.799
+important nowadays uh that models are
+
+00:52:04.200 --> 00:52:07.880
+being deployed to real people in the
+
+00:52:05.799 --> 00:52:09.880
+real world and like actually causing
+
+00:52:07.880 --> 00:52:11.760
+harm to people in some cases that we
+
+00:52:09.880 --> 00:52:15.160
+need to be worried about
+
+00:52:11.760 --> 00:52:17.000
+that Advanced Training in architectures
+
+00:52:15.160 --> 00:52:19.280
+so we're going to talk about distill
+
+00:52:17.000 --> 00:52:21.400
+distillation and quantization how can we
+
+00:52:19.280 --> 00:52:23.520
+make small language models uh that
+
+00:52:21.400 --> 00:52:24.880
+actually still work well like not large
+
+00:52:23.520 --> 00:52:27.559
+you can run them on your phone you can
+
+00:52:24.880 --> 00:52:29.920
+run them on your local
+
+00:52:27.559 --> 00:52:31.640
+laptop um ensembling and mixtures of
+
+00:52:29.920 --> 00:52:33.480
+experts how can we combine together
+
+00:52:31.640 --> 00:52:34.760
+multiple models in order to create
+
+00:52:33.480 --> 00:52:35.880
+models that are better than the sum of
+
+00:52:34.760 --> 00:52:38.799
+their
+
+00:52:35.880 --> 00:52:40.720
+parts and um retrieval and retrieval
+
+00:52:38.799 --> 00:52:43.920
+augmented
+
+00:52:40.720 --> 00:52:45.480
+generation long sequence models uh so
+
+00:52:43.920 --> 00:52:49.920
+how do we handle long
+
+00:52:45.480 --> 00:52:52.240
+outputs um and uh we're going to talk
+
+00:52:49.920 --> 00:52:55.760
+about applications to complex reasoning
+
+00:52:52.240 --> 00:52:57.760
+tasks code generation language agents
+
+00:52:55.760 --> 00:52:59.920
+and knowledge-based QA and information
+
+00:52:57.760 --> 00:53:04.160
+extraction I picked
+
+00:52:59.920 --> 00:53:06.760
+these because they seem to be maybe the
+
+00:53:04.160 --> 00:53:09.880
+most important at least in research
+
+00:53:06.760 --> 00:53:11.440
+nowadays and also they cover uh the
+
+00:53:09.880 --> 00:53:13.640
+things that when I talk to people in
+
+00:53:11.440 --> 00:53:15.280
+Industry are kind of most interested in
+
+00:53:13.640 --> 00:53:17.559
+so hopefully it'll be useful regardless
+
+00:53:15.280 --> 00:53:19.799
+of uh whether you plan on doing research
+
+00:53:17.559 --> 00:53:22.839
+or or plan on doing industry related
+
+00:53:19.799 --> 00:53:24.160
+things uh by by the way the two things
+
+00:53:22.839 --> 00:53:25.920
+that when I talk to people in Industry
+
+00:53:24.160 --> 00:53:29.599
+they're most interested in are Rag and
+
+00:53:25.920 --> 00:53:31.079
+code generation at the moment for now um
+
+00:53:29.599 --> 00:53:32.319
+so those are ones that you'll want to
+
+00:53:31.079 --> 00:53:34.680
+pay attention
+
+00:53:32.319 --> 00:53:36.599
+to and then finally we have a few
+
+00:53:34.680 --> 00:53:40.079
+lectures on Linguistics and
+
+00:53:36.599 --> 00:53:42.720
+multilinguality um I love Linguistics
+
+00:53:40.079 --> 00:53:44.839
+but uh to be honest at the moment most
+
+00:53:42.720 --> 00:53:47.760
+of our Cutting Edge models don't
+
+00:53:44.839 --> 00:53:49.240
+explicitly use linguistic structure um
+
+00:53:47.760 --> 00:53:50.799
+but I still think it's useful to know
+
+00:53:49.240 --> 00:53:52.760
+about it especially if you're working on
+
+00:53:50.799 --> 00:53:54.880
+multilingual things especially if you're
+
+00:53:52.760 --> 00:53:57.040
+interested in very robust generalization
+
+00:53:54.880 --> 00:53:58.920
+to new models so we're going to talk a
+
+00:53:57.040 --> 00:54:02.599
+little bit about that and also
+
+00:53:58.920 --> 00:54:06.079
+multilingual LP I'm going to have
+
+00:54:02.599 --> 00:54:09.119
+fure so also if you have any suggestions
+
+00:54:06.079 --> 00:54:11.400
+um we have two guest lecture slots still
+
+00:54:09.119 --> 00:54:12.799
+open uh that I'm trying to fill so if
+
+00:54:11.400 --> 00:54:15.440
+you have any things that you really want
+
+00:54:12.799 --> 00:54:16.440
+to hear about um I could either add them
+
+00:54:15.440 --> 00:54:19.319
+to the
+
+00:54:16.440 --> 00:54:21.079
+existing you know content or I could
+
+00:54:19.319 --> 00:54:23.240
+invite a guest lecturer who's working on
+
+00:54:21.079 --> 00:54:24.079
+that topic so you know please feel free
+
+00:54:23.240 --> 00:54:26.760
+to tell
+
+00:54:24.079 --> 00:54:29.160
+me um then the class format and
+
+00:54:26.760 --> 00:54:32.280
+structure uh the class
+
+00:54:29.160 --> 00:54:34.000
+content my goal is to learn in detail
+
+00:54:32.280 --> 00:54:36.640
+about building NLP systems from a
+
+00:54:34.000 --> 00:54:40.520
+research perspective so this is a 700
+
+00:54:36.640 --> 00:54:43.599
+level course so it's aiming to be for
+
+00:54:40.520 --> 00:54:46.960
+people who really want to try new and
+
+00:54:43.599 --> 00:54:49.280
+Innovative things in uh kind of natural
+
+00:54:46.960 --> 00:54:51.359
+language processing it's not going to
+
+00:54:49.280 --> 00:54:52.760
+focus solely on reimplementing things
+
+00:54:51.359 --> 00:54:54.319
+that have been done before including in
+
+00:54:52.760 --> 00:54:55.280
+the project I'm going to be expecting
+
+00:54:54.319 --> 00:54:58.480
+everybody to do something something
+
+00:54:55.280 --> 00:54:59.920
+that's kind of new whether it's coming
+
+00:54:58.480 --> 00:55:01.359
+up with a new method or applying
+
+00:54:59.920 --> 00:55:03.559
+existing methods to a place where they
+
+00:55:01.359 --> 00:55:05.079
+haven't been used before or building out
+
+00:55:03.559 --> 00:55:06.640
+things for a new language or something
+
+00:55:05.079 --> 00:55:08.359
+like that so that's kind of one of the
+
+00:55:06.640 --> 00:55:11.480
+major goals of this
+
+00:55:08.359 --> 00:55:13.000
+class um learn basic and advanced topics
+
+00:55:11.480 --> 00:55:15.559
+in machine learning approaches to NLP
+
+00:55:13.000 --> 00:55:18.359
+and language models learn some basic
+
+00:55:15.559 --> 00:55:21.480
+linguistic knowledge useful in NLP uh
+
+00:55:18.359 --> 00:55:23.200
+see case studies of NLP applications and
+
+00:55:21.480 --> 00:55:25.680
+learn how to identify unique problems
+
+00:55:23.200 --> 00:55:29.039
+for each um one thing i' like to point
+
+00:55:25.680 --> 00:55:31.160
+out is I'm not going to cover every NLP
+
+00:55:29.039 --> 00:55:32.920
+application ever because that would be
+
+00:55:31.160 --> 00:55:35.520
+absolutely impossible NLP is being used
+
+00:55:32.920 --> 00:55:37.079
+in so many different areas nowadays but
+
+00:55:35.520 --> 00:55:38.960
+what I want people to pay attention to
+
+00:55:37.079 --> 00:55:41.280
+like even if you're not super interested
+
+00:55:38.960 --> 00:55:42.400
+in code generation for example what you
+
+00:55:41.280 --> 00:55:44.200
+can do is you can look at code
+
+00:55:42.400 --> 00:55:46.160
+generation look at how people identify
+
+00:55:44.200 --> 00:55:47.680
+problems look at the methods that people
+
+00:55:46.160 --> 00:55:50.880
+have proposed to solve those unique
+
+00:55:47.680 --> 00:55:53.039
+problems and then kind of map that try
+
+00:55:50.880 --> 00:55:54.799
+to do some generalization onto your own
+
+00:55:53.039 --> 00:55:57.799
+problems of Interest so uh that's kind
+
+00:55:54.799 --> 00:56:00.280
+of the goal of the NLP
+
+00:55:57.799 --> 00:56:02.440
+applications finally uh learning how to
+
+00:56:00.280 --> 00:56:05.160
+debug when and where NLP systems fail
+
+00:56:02.440 --> 00:56:08.200
+and build improvements based on this so
+
+00:56:05.160 --> 00:56:10.200
+um ever since I was a graduate student
+
+00:56:08.200 --> 00:56:12.720
+this has been like one of the really
+
+00:56:10.200 --> 00:56:15.920
+important things that I feel like I've
+
+00:56:12.720 --> 00:56:17.440
+done well or done better than some other
+
+00:56:15.920 --> 00:56:19.280
+people and I I feel like it's a really
+
+00:56:17.440 --> 00:56:21.119
+good way to like even if you're only
+
+00:56:19.280 --> 00:56:22.680
+interested in improving accuracy knowing
+
+00:56:21.119 --> 00:56:25.039
+why your system's failing still is the
+
+00:56:22.680 --> 00:56:27.599
+best way to do that I so I'm going to
+
+00:56:25.039 --> 00:56:30.559
+put a lot of emphasis on
+
+00:56:27.599 --> 00:56:32.559
+that in terms of the class format um
+
+00:56:30.559 --> 00:56:36.280
+before class for some classes there are
+
+00:56:32.559 --> 00:56:37.880
+recommended reading uh this can be
+
+00:56:36.280 --> 00:56:39.559
+helpful to read I'm never going to
+
+00:56:37.880 --> 00:56:41.119
+expect you to definitely have read it
+
+00:56:39.559 --> 00:56:42.480
+before the class but I would suggest
+
+00:56:41.119 --> 00:56:45.160
+that maybe you'll get more out of the
+
+00:56:42.480 --> 00:56:47.319
+class if you do that um during class
+
+00:56:45.160 --> 00:56:48.079
+we'll have the lecture um in discussion
+
+00:56:47.319 --> 00:56:50.559
+with
+
+00:56:48.079 --> 00:56:52.359
+everybody um sometimes we'll have a code
+
+00:56:50.559 --> 00:56:55.839
+or data walk
+
+00:56:52.359 --> 00:56:58.760
+um actually this is a a little bit old I
+
+00:56:55.839 --> 00:57:01.880
+I have this slide we're this year we're
+
+00:56:58.760 --> 00:57:04.160
+going to be adding more uh code and data
+
+00:57:01.880 --> 00:57:07.400
+walks during office hours and the way it
+
+00:57:04.160 --> 00:57:09.400
+will work is one of the Tas we have
+
+00:57:07.400 --> 00:57:11.160
+seven Tas who I'm going to introduce
+
+00:57:09.400 --> 00:57:15.000
+very soon but one of the Tas will be
+
+00:57:11.160 --> 00:57:16.839
+doing this kind of recitation where you
+
+00:57:15.000 --> 00:57:18.200
+um where we go over a library so if
+
+00:57:16.839 --> 00:57:19.480
+you're not familiar with the library and
+
+00:57:18.200 --> 00:57:21.960
+you want to be more familiar with the
+
+00:57:19.480 --> 00:57:23.720
+library you can join this and uh then
+
+00:57:21.960 --> 00:57:25.400
+we'll be able to do this and this will
+
+00:57:23.720 --> 00:57:28.240
+cover things like
+
+00:57:25.400 --> 00:57:31.039
+um pie torch and sentence piece uh we're
+
+00:57:28.240 --> 00:57:33.280
+going to start out with hugging face um
+
+00:57:31.039 --> 00:57:36.559
+inference stuff like
+
+00:57:33.280 --> 00:57:41.520
+VM uh debugging software like
+
+00:57:36.559 --> 00:57:41.520
+Xeno um what were the other
+
+00:57:41.960 --> 00:57:47.200
+ones oh the open AI API and light llm
+
+00:57:45.680 --> 00:57:50.520
+other stuff like that so we we have lots
+
+00:57:47.200 --> 00:57:53.599
+of them planned we'll uh uh we'll update
+
+00:57:50.520 --> 00:57:54.839
+that um and then after class after
+
+00:57:53.599 --> 00:57:58.079
+almost every class we'll have a question
+
+00:57:54.839 --> 00:58:00.079
+quiz um and the quiz is intended to just
+
+00:57:58.079 --> 00:58:02.000
+you know make sure that you uh paid
+
+00:58:00.079 --> 00:58:04.480
+attention to the material and are able
+
+00:58:02.000 --> 00:58:07.520
+to answer questions about it we will aim
+
+00:58:04.480 --> 00:58:09.559
+to release it on the day of the course
+
+00:58:07.520 --> 00:58:11.599
+the day of the actual lecture and it
+
+00:58:09.559 --> 00:58:14.559
+will be due at the end of the following
+
+00:58:11.599 --> 00:58:15.960
+day of the lecture so um it will be
+
+00:58:14.559 --> 00:58:18.920
+three questions it probably shouldn't
+
+00:58:15.960 --> 00:58:20.680
+take a whole lot of time but um uh yeah
+
+00:58:18.920 --> 00:58:23.400
+so we'll H
+
+00:58:20.680 --> 00:58:26.319
+that in terms of assignments assignment
+
+00:58:23.400 --> 00:58:28.640
+one is going to be build your own llama
+
+00:58:26.319 --> 00:58:30.200
+and so what this is going to look like
+
+00:58:28.640 --> 00:58:32.680
+is we're going to give you a partial
+
+00:58:30.200 --> 00:58:34.319
+implementation of llama which is kind of
+
+00:58:32.680 --> 00:58:37.960
+the most popular open source language
+
+00:58:34.319 --> 00:58:40.160
+model nowadays and ask you to fill in um
+
+00:58:37.960 --> 00:58:42.839
+ask you to fill in the parts we're going
+
+00:58:40.160 --> 00:58:45.920
+to train a very small version of llama
+
+00:58:42.839 --> 00:58:47.319
+on a small data set and get it to work
+
+00:58:45.920 --> 00:58:48.880
+and the reason why it's very small is
+
+00:58:47.319 --> 00:58:50.480
+because the smallest actual version of
+
+00:58:48.880 --> 00:58:53.039
+llama is 7 billion
+
+00:58:50.480 --> 00:58:55.359
+parameters um and that might be a little
+
+00:58:53.039 --> 00:58:58.400
+bit difficult to train with
+
+00:58:55.359 --> 00:59:00.680
+resources um for assignment two we're
+
+00:58:58.400 --> 00:59:04.559
+going to try to do an NLP task from
+
+00:59:00.680 --> 00:59:06.920
+scratch and so the way this will work is
+
+00:59:04.559 --> 00:59:08.520
+we're going to give you an assignment
+
+00:59:06.920 --> 00:59:10.880
+which we're not going to give you an
+
+00:59:08.520 --> 00:59:13.400
+actual data set and instead we're going
+
+00:59:10.880 --> 00:59:15.760
+to ask you to uh perform data creation
+
+00:59:13.400 --> 00:59:19.359
+modeling and evaluation for a specified
+
+00:59:15.760 --> 00:59:20.640
+task and so we're going to tell you uh
+
+00:59:19.359 --> 00:59:22.599
+what to do but we're not going to tell
+
+00:59:20.640 --> 00:59:26.400
+you exactly how to do it but we're going
+
+00:59:22.599 --> 00:59:29.680
+to try to give as conrete directions as
+
+00:59:26.400 --> 00:59:32.359
+we can um
+
+00:59:29.680 --> 00:59:34.160
+yeah will you be given a parameter limit
+
+00:59:32.359 --> 00:59:36.559
+on the model so that's a good question
+
+00:59:34.160 --> 00:59:39.119
+or like a expense limit or something
+
+00:59:36.559 --> 00:59:40.440
+like that um I maybe actually I should
+
+00:59:39.119 --> 00:59:44.240
+take a break from the assignments and
+
+00:59:40.440 --> 00:59:46.520
+talk about compute so right now um for
+
+00:59:44.240 --> 00:59:49.319
+assignment one we're planning on having
+
+00:59:46.520 --> 00:59:51.599
+this be able to be done either on a Mac
+
+00:59:49.319 --> 00:59:53.520
+laptop with an M1 or M2 processor which
+
+00:59:51.599 --> 00:59:57.079
+I think a lot of people have or Google
+
+00:59:53.520 --> 00:59:59.839
+collab um so it should be like
+
+00:59:57.079 --> 01:00:02.160
+sufficient to use free computational
+
+00:59:59.839 --> 01:00:03.640
+resources that you have for number two
+
+01:00:02.160 --> 01:00:06.079
+we'll think about that I think that's
+
+01:00:03.640 --> 01:00:08.280
+important we do have Google cloud
+
+01:00:06.079 --> 01:00:11.520
+credits for $50 for everybody and I'm
+
+01:00:08.280 --> 01:00:13.440
+working to get AWS credits for more um
+
+01:00:11.520 --> 01:00:18.160
+but the cloud providers nowadays are
+
+01:00:13.440 --> 01:00:19.680
+being very stingy so um so it's uh been
+
+01:00:18.160 --> 01:00:22.160
+a little bit of a fight to get uh
+
+01:00:19.680 --> 01:00:23.680
+credits but I I it is very important so
+
+01:00:22.160 --> 01:00:28.480
+I'm going to try to get as as many as we
+
+01:00:23.680 --> 01:00:31.119
+can um and so yeah I I think basically
+
+01:00:28.480 --> 01:00:32.280
+uh there will be some sort of like limit
+
+01:00:31.119 --> 01:00:34.480
+on the amount of things you can
+
+01:00:32.280 --> 01:00:36.240
+practically do and so because of that
+
+01:00:34.480 --> 01:00:39.920
+I'm hoping that people will rely very
+
+01:00:36.240 --> 01:00:43.359
+heavily on pre-trained models um or uh
+
+01:00:39.920 --> 01:00:46.079
+yeah pre-trained models
+
+01:00:43.359 --> 01:00:49.599
+and yeah so that that's the the short
+
+01:00:46.079 --> 01:00:52.799
+story B um the second thing uh the
+
+01:00:49.599 --> 01:00:54.720
+assignment three is to do a survey of
+
+01:00:52.799 --> 01:00:57.920
+some sort of state-ofthe-art research
+
+01:00:54.720 --> 01:01:00.760
+resarch and do a reimplementation of
+
+01:00:57.920 --> 01:01:02.000
+this and in doing this again you will
+
+01:01:00.760 --> 01:01:03.440
+have to think about something that's
+
+01:01:02.000 --> 01:01:06.359
+feasible within computational
+
+01:01:03.440 --> 01:01:08.680
+constraints um and so you can discuss
+
+01:01:06.359 --> 01:01:11.839
+with your Tas about uh about the best
+
+01:01:08.680 --> 01:01:13.920
+way to do this um and then the final
+
+01:01:11.839 --> 01:01:15.400
+project is to perform a unique project
+
+01:01:13.920 --> 01:01:17.559
+that either improves on the state-of-the
+
+01:01:15.400 --> 01:01:21.000
+art with respect to whatever you would
+
+01:01:17.559 --> 01:01:23.440
+like to improve with this could be uh
+
+01:01:21.000 --> 01:01:25.280
+accuracy for sure this could be
+
+01:01:23.440 --> 01:01:27.760
+efficiency
+
+01:01:25.280 --> 01:01:29.599
+it could be some sense of
+
+01:01:27.760 --> 01:01:31.520
+interpretability but if it's going to be
+
+01:01:29.599 --> 01:01:33.599
+something like interpretability you'll
+
+01:01:31.520 --> 01:01:35.440
+have to discuss with us what that means
+
+01:01:33.599 --> 01:01:37.240
+like how we measure that how we can like
+
+01:01:35.440 --> 01:01:40.839
+actually say that you did a good job
+
+01:01:37.240 --> 01:01:42.839
+with improving that um another thing
+
+01:01:40.839 --> 01:01:44.680
+that you can do is take whatever you
+
+01:01:42.839 --> 01:01:47.280
+implemented for assignment 3 and apply
+
+01:01:44.680 --> 01:01:49.039
+it to a new task or apply it to a new
+
+01:01:47.280 --> 01:01:50.760
+language that has never been examined
+
+01:01:49.039 --> 01:01:53.119
+before so these are also acceptable
+
+01:01:50.760 --> 01:01:54.240
+final projects but basically the idea is
+
+01:01:53.119 --> 01:01:55.559
+for the final project you need to do
+
+01:01:54.240 --> 01:01:57.480
+something something new that hasn't been
+
+01:01:55.559 --> 01:01:59.880
+done before and create new knowledge
+
+01:01:57.480 --> 01:02:04.520
+with the respect
+
+01:01:59.880 --> 01:02:07.640
+toy um so for this the instructor is me
+
+01:02:04.520 --> 01:02:09.920
+um I'm uh looking forward to you know
+
+01:02:07.640 --> 01:02:13.599
+discussing and working with all of you
+
+01:02:09.920 --> 01:02:16.119
+um for TAS we have seven Tas uh two of
+
+01:02:13.599 --> 01:02:18.319
+them are in transit so they're not here
+
+01:02:16.119 --> 01:02:22.279
+today um the other ones uh Tas would you
+
+01:02:18.319 --> 01:02:22.279
+mind coming up uh to introduce
+
+01:02:23.359 --> 01:02:26.359
+yourself
+
+01:02:28.400 --> 01:02:32.839
+so um yeah nhir and akshai couldn't be
+
+01:02:31.599 --> 01:02:34.039
+here today because they're traveling
+
+01:02:32.839 --> 01:02:37.119
+I'll introduce them later because
+
+01:02:34.039 --> 01:02:37.119
+they're coming uh next
+
+01:02:40.359 --> 01:02:46.480
+time cool and what I'd like everybody to
+
+01:02:43.000 --> 01:02:48.680
+do is say um like you know what your
+
+01:02:46.480 --> 01:02:53.079
+name is uh what
+
+01:02:48.680 --> 01:02:55.799
+your like maybe what you're interested
+
+01:02:53.079 --> 01:02:57.319
+in um and the reason the goal of this is
+
+01:02:55.799 --> 01:02:59.200
+number one for everybody to know who you
+
+01:02:57.319 --> 01:03:00.720
+are and number two for everybody to know
+
+01:02:59.200 --> 01:03:03.440
+who the best person to talk to is if
+
+01:03:00.720 --> 01:03:03.440
+they're interested in
+
+01:03:04.200 --> 01:03:09.079
+particular hi uh I'm
+
+01:03:07.000 --> 01:03:15.520
+Aila second
+
+01:03:09.079 --> 01:03:15.520
+year I work on language and social
+
+01:03:16.200 --> 01:03:24.559
+and I'm I'm a second this year PhD
+
+01:03:21.160 --> 01:03:26.799
+student Grand and Shar with you I search
+
+01:03:24.559 --> 01:03:28.480
+is like started in the border of MP and
+
+01:03:26.799 --> 01:03:31.000
+computer interaction with a lot of work
+
+01:03:28.480 --> 01:03:32.640
+on automating parts of the developer
+
+01:03:31.000 --> 01:03:35.319
+experience to make it easier for anyone
+
+01:03:32.640 --> 01:03:35.319
+to
+
+01:03:39.090 --> 01:03:42.179
+[Music]
+
+01:03:47.520 --> 01:03:53.279
+orif
+
+01:03:50.079 --> 01:03:54.680
+everyone first
+
+01:03:53.279 --> 01:03:57.119
+year
+
+01:03:54.680 --> 01:04:00.119
+[Music]
+
+01:03:57.119 --> 01:04:03.559
+I don't like updating primar models I
+
+01:04:00.119 --> 01:04:03.559
+hope to not update Prim
+
+01:04:14.599 --> 01:04:19.400
+modelm yeah thanks a lot everyone and
+
+01:04:17.200 --> 01:04:19.400
+yeah
+
+01:04:20.839 --> 01:04:29.400
+than and so we will um we'll have people
+
+01:04:25.640 --> 01:04:30.799
+uh kind of have office hours uh every ta
+
+01:04:29.400 --> 01:04:32.880
+has office hours at a regular time
+
+01:04:30.799 --> 01:04:34.480
+during the week uh please feel free to
+
+01:04:32.880 --> 01:04:38.400
+come to their office hours or my office
+
+01:04:34.480 --> 01:04:41.960
+hours um I think they are visha are they
+
+01:04:38.400 --> 01:04:43.880
+posted on the site or okay yeah they
+
+01:04:41.960 --> 01:04:47.240
+they either are or will be posted on the
+
+01:04:43.880 --> 01:04:49.720
+site very soon um and come by to talk
+
+01:04:47.240 --> 01:04:51.480
+about anything uh if there's nobody in
+
+01:04:49.720 --> 01:04:53.079
+my office hours I'm happy to talk about
+
+01:04:51.480 --> 01:04:54.599
+things that are unrelated but if there's
+
+01:04:53.079 --> 01:04:58.039
+lots of people waiting outside or I
+
+01:04:54.599 --> 01:05:00.319
+might limit it to uh like um just things
+
+01:04:58.039 --> 01:05:02.480
+about the class so cool and we have
+
+01:05:00.319 --> 01:05:04.760
+Patza we'll be checking that regularly
+
+01:05:02.480 --> 01:05:06.839
+uh striving to get you an answer in 24
+
+01:05:04.760 --> 01:05:12.240
+hours on weekdays over weekends we might
+
+01:05:06.839 --> 01:05:16.000
+not so um yeah so that's all for today
+
+01:05:12.240 --> 01:05:16.000
+are there any questions