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