No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

Abstract: Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems.

adversarial reward learning
caption
human evaluation
policy gradient


Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing

Abstract: In this paper, we present a straightforward strategy for transferring dependency parsers across languages. The proposed method learns a parser from partially annotated data obtained through the projection of annotations across unambiguous word alignments. It does not rely on any modeling of the reliability of dependency and/or alignment links and is therefore easy to implement and parameter free. Experiments on six languages show that our method is at par with recent algorithmically demanding methods, at a much cheaper computational cost. It can thus serve as a fair baseline for transferring dependencies across languages with the use of parallel corpora.

cross-lingual transfer
dependency parser
alignment link
parallel corpora
annotation projection


Calibrating Structured Output Predictors for Natural Language Processing

Abstract: We address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition and question answering produce calibrated confidence scores for their predictions, especially if the applications are to be deployed in a safety-critical domain such as healthcare. However the output space of such structured prediction models are often too large to directly adapt binary or multi-class calibration methods. In this study, we propose a general calibration scheme for output entities of interest in neural network based structured prediction models. Our proposed method can be used with any binary class calibration scheme and a neural network model. Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements. We show that our method outperforms current calibration techniques for Named Entity Recognition, Part-of-speech tagging and Question Answering systems. We also observe an improvement in model performance from our decoding step across several tasks and benchmark datasets. Our method improves the calibration and model performance on out-of-domain test scenarios as well.

output entity
calibration
forecaster
decision tree


Operation-guided Neural Networks for High Fidelity Data-To-Text Generation

Abstract: Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.

pre-executed symbolic operation
neural table-to-text generation
text generation


Small and Practical BERT Models for Sequence Labeling

Abstract: We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU. Starting from a public multilingual BERT checkpoint, our final model is 6x smaller and 27x faster, and has higher accuracy than a state-of-the-art multilingual baseline. We show that our model especially outperforms on low-resource languages, and works on codemixed input text without being explicitly trained on codemixed examples. We showcase the effectiveness of our method by reporting on part-of-speech tagging and morphological prediction on 70 treebanks and 48 languages.

sequence labeling
single cpu
m-bert model


Adapting to Learner Errors with Minimal Supervision

Abstract: This article considers the problem of correcting errors made by English as a Second Language writers from a machine learning perspective, and addresses an important issue of developing an appropriate training paradigm for the task, one that accounts for error patterns of non-native writers using minimal supervision. Existing training approaches present a trade-off between large amounts of cheap data offered by the native-trained models and additional knowledge of learner error patterns provided by the more expensive method of training on annotated learner data. We propose a novel training approach that draws on the strengths offered by the two standard training paradigms—of training either on native or on annotated learner data—and that outperforms both of these standard methods. Using the key observation that parameters relating to error regularities exhibited by non-native writers are relatively simple, we develop models that can incorporate knowledge about error regularities based on a small annotated sample but that are otherwise trained on native English data. The key contribution of this article is the introduction and analysis of two methods for adapting the learned models to error patterns of non-native writers; one method that applies to generative classifiers and a second that applies to discriminative classifiers. Both methods demonstrated state-of-the-art performance in several text correction competitions. In particular, the Illinois system that implements these methods ranked at the top in two recent CoNLL shared tasks on error correction.1 We conduct further evaluation of the proposed approaches studying the effect of using error data from speakers of the same native language, languages that are closely related linguistically, and unrelated languages.

minimal supervision
learner error pattern
error type


Collaborative Dialogue in Minecraft

Abstract: We wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. Since computer games allow us to simulate such tasks without the need for physical robots, we define a Minecraft-based collaborative building task in which one player (A, the Architect) is shown a target structure and needs to instruct the other player (B, the Builder) to build this structure. Both players interact via a chat interface. A can observe B but cannot place blocks. We present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs. As a first step towards our goal of developing fully interactive agents for this task, we consider the subtask of Architect utterance generation, and show how challenging it is.

collaborative building task
minecraft dialogue corpus
dialog


Has machine translation improved? some historical comparisons

Abstract: The common assertion that MT systems have improved over the last decades is examined by informal comparisons of translations produced by operational systems in the 1960s, 1970s and 1980s and of translations of the same source texts produced by some currently available commercial and online systems. The scarcity of source and target texts for earlier systems means that the conclusions are consequently tentative and preliminary.


On Sparsifying Encoder Outputs in Sequence-to-Sequence Models

Abstract: Sequence-to-sequence models usually transfer all encoder outputs to the decoder for generation. In this work, by contrast, we hypothesize that these encoder outputs can be compressed to shorten the sequence delivered for decoding. We take Transformer as the testbed and introduce a layer of stochastic gates in-between the encoder and the decoder. The gates are regularized using the expected value of the sparsity-inducing L0penalty, resulting in completely masking-out a subset of encoder outputs. In other words, via joint training, the L0DROP layer forces Transformer to route information through a subset of its encoder states. We investigate the effects of this sparsification on two machine translation and two summarization tasks. Experiments show that, depending on the task, around 40-70% of source encodings can be pruned without significantly compromising quality. The decrease of the output length endows L0DROP with the potential of improving decoding efficiency, where it yields a speedup of up to 1.65x on document summarization tasks against the standard Transformer. We analyze the L0DROP behaviour and observe that it exhibits systematic preferences for pruning certain word types, e.g., function words and punctuation get pruned most. Inspired by these observations, we explore the feasibility of specifying rule-based patterns that mask out encoder outputs based on information such as part-of-speech tags, word frequency and word position.

encoder output
sequence-to-sequence model
summarization task


Agreement on Target-bidirectional Neural Machine Translation

Abstract: Neural machine translation (NMT) with recurrent neural networks, has proven to be an effective technique for end-to-end machine translation. However, in spite of its promising advances over traditional translation methods, it typically suffers from an issue of unbalanced outputs, that arise from both the nature of recurrent neural networks themselves, and the challenges inherent in machine translation. To overcome this issue, we propose an agreement model for neural machine translation and show its effectiveness on large-scale Japaneseto-English and Chinese-to-English translation tasks. Our results show the model can achieve improvements of up to 1.4 BLEU over the strongest baseline NMT system. With the help of an ensemble technique, this new end-to-end NMT approach finally outperformed phrasebased and hierarchical phrase-based Moses baselines by up to 5.6 BLEU points.

neural machine translation
bidirectional decoding
suffix


Personalized Page Rank for Named Entity Disambiguation

Abstract: The task of Named Entity Disambiguation is to map entity mentions in the document to their correct entries in some knowledge base. We present a novel graph-based disambiguation approach based on Personalized PageRank (PPR) that combines local and global evidence for disambiguation and effectively filters out noise introduced by incorrect candidates. Experiments show that our method outperforms state-of-the-art approaches by achieving 91.7% in microand 89.9% in macroaccuracy on a dataset of 27.8K named entity mentions.

named entity disambiguation
personalized pagerank
candidate


Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples

Abstract: Recent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood. In this work, we show that adversarial attacks against CNN, LSTM and Transformer-based classification models perform word substitutions that are identifiable through frequency differences between replaced words and their corresponding substitutions. Based on these findings, we propose frequency-guided word substitutions (FGWS), a simple algorithm exploiting the frequency properties of adversarial word substitutions for the detection of adversarial examples. FGWS achieves strong performance by accurately detecting adversarial examples on the SST-2 and IMDb sentiment datasets, with F1 detection scores of up to 91.4% against RoBERTa-based classification models. We compare our approach against a recently proposed perturbation discrimination framework and show that we outperform it by up to 13.0% F1.

detection
frequency-guided word substitution
adversarial sample


Linguistic Considerations in Automatic Question Generation

Abstract: This paper describes an automatic question generator that uses semantic pattern recognition to create questions of varying depth and type for self-study or tutoring.

automatic question generation
tutoring
heuristic rule


ConveRT: Efficient and Accurate Conversational Representations from Transformers

Abstract: General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train. We pretrain using a retrieval-based response selection task, effectively leveraging quantization and subword-level parameterization in the dual encoder to build a lightweight memory- and energy-efficient model. We show that ConveRT achieves state-of-the-art performance across widely established response selection tasks. We also demonstrate that the use of extended dialog history as context yields further performance gains. Finally, we show that pretrained representations from the proposed encoder can be transferred to the intent classification task, yielding strong results across three diverse data sets. ConveRT trains substantially faster than standard sentence encoders or previous state-of-the-art dual encoders. With its reduced size and superior performance, we believe this model promises wider portability and scalability for Conversational AI applications.

conversational representations
language model
generation task
original pre-training task


Towards Mediating Shared Perceptual Basis in Situated Dialogue

Abstract: To enable effective referential grounding in situated human robot dialogue, we have conducted an empirical study to investigate how conversation partners collaborate and mediate shared basis when they have mismatched visual perceptual capabilities. In particular, we have developed a graph-based representation to capture linguistic discourse and visual discourse, and applied inexact graph matching to ground references. Our empirical results have shown that, even when computer vision algorithms produce many errors (e.g. 84.7% of the objects in the environment are mis-recognized), our approach can still achieve 66% accuracy in referential grounding. These results demonstrate that, due to its error-tolerance nature, inexact graph matching provides a potential solution to mediate shared perceptual basis for referential grounding in situated interaction.

situated dialogue
referential grounding
perception


Cue Me In: Content-Inducing Approaches to Interactive Story Generation

Abstract: Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user. Here, we instead focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions in the form of cue phrases during the generation process. This provides an interface for human users to guide the story generation. We present two content-inducing approaches to effectively incorporate this additional information. Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories compared to baseline methods.

interactive story generation
cue phrase
generation process


A Challenge Set for Advancing Language Modeling

Abstract: In this paper, we describe a new, publicly available corpus intended to stimulate research into language modeling techniques which are sensitive to overall sentence coherence. The task uses the Scholastic Aptitude Test's sentence completion format. The test set consists of 1040 sentences, each of which is missing a content word. The goal is to select the correct replacement from amongst five alternates. In general, all of the options are syntactically valid, and reasonable with respect to local N-gram statistics. The set was generated by using an N-gram language model to generate a long list of likely words, given the immediate context. These options were then hand-groomed, to identify four decoys which are globally incoherent, yet syntactically correct. To ensure the right to public distribution, all the data is derived from out-of-copyright materials from Project Gutenberg. The test sentences were derived from five of Conan Doyle's Sherlock Holmes novels, and we provide a large set of Nineteenth and early Twentieth Century texts as training material.

language modeling
immediate context
sentence completion challenge


Neural Generative Question Answering

Abstract: This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.

neural network model
factoid question
encoder-decoder framework
seq2seq learning


Unsupervised Semantic Abstractive Summarization

Abstract: Automatic abstractive summary generation remains a significant open problem for natural language processing. In this work, we develop a novel pipeline for Semantic Abstractive Summarization (SAS). SAS, as introduced by Liu et. al. (2015) first generates an AMR graph of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7% F1 score in node prediction.


Annotations for Power Relations on Email Threads

Abstract: Social relations like power and influence are difficult concepts to define, but are easily recognizable when expressed. In this paper, we describe a multi-layer annotation scheme for social power relations that are recognizable from online written interactions. We introduce a typology of four types of power relations between dialog participants: hierarchical power, situational power, influence and control of communication. We also present a corpus of Enron emails comprising of 122 threaded conversations, manually annotated with instances of these power relations between participants. Our annotations also capture attempts at exercise of power or influence and whether those attempts were successful or not. In addition, we also capture utterance level annotations for overt display of power. We describe the annotation definitions using two example email threads from our corpus illustrating each type of power relation. We also present detailed instructions given to the annotators and provide various statistics on annotations in the corpus.

email thread
social relation
overt display


Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination

Abstract: With great practical value, the study of Multi-domain Neural Machine Translation (NMT) mainly focuses on using mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. Intuitively, words in a sentence are related to its domain to varying degrees, so that they will exert disparate impacts on the multi-domain NMT modeling. Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT. To this end, we jointly model NMT with monolingual attention-based domain classification tasks and improve NMT as follows: 1) Based on the sentence representations produced by a domain classifier and an adversarial domain classifier, we generate two gating vectors and use them to construct domain-specific and domain-shared annotations, for later translation predictions via different attention models; 2) We utilize the attention weights derived from target-side domain classifier to adjust the weights of target words in the training objective, enabling domain-related words to have greater impacts during model training. Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. Source codes of this paper are available on Github <https://github.com/DeepLearnXMU/WDCNMT>.

machine translation
adversarial domain classifier
domain-agnostic representation


Multi-modal Discriminative Model for Vision-and-Language Navigation

Abstract: Vision-and-Language Navigation (VLN) is a natural language grounding task where agents have to interpret natural language instructions in the context of visual scenes in a dynamic environment to achieve prescribed navigation goals. Successful agents must have the ability to parse natural language of varying linguistic styles, ground them in potentially unfamiliar scenes, plan and react with ambiguous environmental feedback. Generalization ability is limited by the amount of human annotated data. In particular, paired vision-language sequence data is expensive to collect. We develop a discriminator that evaluates how well an instruction explains a given path in VLN task using multi-modal alignment. Our study reveals that only a small fraction of the high-quality augmented data from Fried et al., as scored by our discriminator, is useful for training VLN agents with similar performance. We also show that a VLN agent warm-started with pre-trained components from the discriminator outperforms the benchmark success rates of 35.5 by 10% relative measure.

vision-and-language navigation
instruction
environment


CogNet: A Large-Scale Cognate Database

Abstract: This paper introduces CogNet, a new, large-scale lexical database that provides cognates -words of common origin and meaning- across languages. The database currently contains 3.1 million cognate pairs across 338 languages using 35 writing systems. The paper also describes the automated method by which cognates were computed from publicly available wordnets, with an accuracy evaluated to 94%. Finally, it presents statistics about the cognate data and some initial insights into it, hinting at a possible future exploitation of the resource by various fields of lingustics.

large-scale lexical database
common origin
cognet


Joint Multilingual Supervision for Cross-lingual Entity Linking

Abstract: Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challenging, owing to limited availability of resources as supervision. We address this challenge by developing the first XEL approach that combines supervision from multiple languages jointly. This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language. Extensive evaluation on three benchmark datasets across 8 languages shows that our approach significantly improves over the current state-of-the-art. We also provide analyses in two limited resource settings: (a) zero-shot setting, when no supervision in the target language is available, and in (b) low-resource setting, when some supervision in the target language is available. Our analysis provides insights into the limitations of zero-shot XEL approaches in realistic scenarios, and shows the value of joint supervision in low-resource settings.

cross-lingual entity linking
downstream disambiguation model
english wikipedia


Multi-Modal Models for Concrete and Abstract Concept Meaning

Abstract: Multi-modal models that learn semantic representations from both linguistic and perceptual input outperform language-only models on a range of evaluations, and better reflect human concept acquisition. Most perceptual input to such models corresponds to concrete noun concepts and the superiority of the multi-modal approach has only been established when evaluating on such concepts. We therefore investigate which concepts can be effectively learned by multi-modal models. We show that concreteness determines both which linguistic features are most informative and the impact of perceptual input in such models. We then introduce ridge regression as a means of propagating perceptual information from concrete nouns to more abstract concepts that is more robust than previous approaches. Finally, we present weighted gram matrix combination, a means of combining representations from distinct modalities that outperforms alternatives when both modalities are sufficiently rich.

noun
multi-modal model
visual similarity


Intrinsic Probing through Dimension Selection

Abstract: Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted. To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal. We then probe fastText and BERT for various morphosyntactic attributes across 36 languages. We find that most attributes are reliably encoded by only a few neurons, with fastText concentrating its linguistic structure more than BERT.


Semantic Role Labeling as Syntactic Dependency Parsing

Abstract: We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.

syntactic dependency parsing
srl
joint label


Temporal Common Sense Acquisition with Minimal Supervision

Abstract: Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on such concepts is costly. This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TacoLM, a temporal common sense language model. Our method is shown to give quality predictions of various dimensions of temporal common sense (on UDST and a newly collected dataset from RealNews). It also produces representations of events for relevant tasks such as duration comparison, parent-child relations, event coreference and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the standard BERT. Thus, it will be an important component of temporal NLP.

duration
sense language model
bert
temporal data
data-driven method


An Examination of Cross-Cultural Similarities and Differences from Social Media Data with respect to Language Use

Abstract: We present a methodology for analyzing cross-cultural similarities and differences using language as a medium, love as domain, social media as a data source and 'Terms' and 'Topics' as cultural features. We discuss the techniques necessary for the creation of the social data corpus from which emotion terms have been extracted using NLP techniques. Topics of love discussion were then extracted from the corpus by means of Latent Dirichlet Allocation (LDA). Finally, on the basis of these features, a cross-cultural comparison was carried out. For the purpose of cross-cultural analysis, the experimental focus was on comparing data from a culture from the East (India) with a culture from the West (United States of America). Similarities and differences between these cultures have been analyzed with respect to the usage of emotions, their intensities and the topics used during love discussion in social media.

emotion term
cross-cultural analysis
social medium data


A Database of Narrative Schemas

Abstract: This paper describes a new language resource of events and semantic roles that characterize real-world situations. Narrative schemas contain sets of related events (edit and publish), a temporal ordering of the events (edit before publish), and the semantic roles of the participants (authors publish books). This type of world knowledge was central to early research in natural language understanding, scripts being one of the main formalisms, they represented common sequences of events that occur in the world. Unfortunately, most of this knowledge was hand-coded and time consuming to create. Current machine learning techniques, as well as a new approach to learning through coreference chains, has allowed us to automatically extract rich event structure from open domain text in the form of narrative schemas. The narrative schema resource described in this paper contains approximately 5000 unique events combined into schemas of varying sizes. We describe the resource, how it is learned, and a new evaluation of the coverage of these schemas over unseen documents.

database
narrative schemas
world knowledge
chain
various event mention


Jane: Open Source Hierarchical Translation, Extended with Reordering and Lexicon Models

Abstract:
We present Jane, RWTH's hierarchical phrase-based translation system, which has been open sourced for the scientific community. This system has been in development at RWTH for the last two years and has been successfully applied in different machine translation evaluations. It includes extensions to the hierarchical approach developed by RWTH as well as other research institutions. In this paper we give an overview of its main features.

We also introduce a novel reordering model for the hierarchical phrase-based approach which further enhances translation performance, and analyze the effect some recent extended lexicon models have on the performance of the system.


Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules

Abstract: Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that `inexpensive' is a rephrasing for `expensive' or may not associate `acquire' with `acquires'. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.

vector space
simple language-specific rule
downstream task


Lexical Chain Based Cohesion Models for Document-Level Statistical Machine Translation

Abstract: Lexical chains provide a representation of the lexical cohesion structure of a text. In this paper, we propose two lexical chain based cohesion models to incorporate lexical cohesion into document-level statistical machine translation: 1) a count cohesion model that rewards a hypothesis whenever a chain word occurs in the hypothesis, 2) and a probability cohesion model that further takes chain word translation probabilities into account. We compute lexical chains for each source document to be translated and generate target lexical chains based on the computed source chains via maximum entropy classifiers. We then use the generated target chains to provide constraints for word selection in document-level machine translation through the two proposed lexical chain based cohesion models. We verify the effectiveness of the two models using a hierarchical phrase-based translation system. Experiments on large-scale training data show that they can substantially improve translation quality in terms of BLEU and that the probability cohesion model outperforms previous models based on lexical cohesion devices.

chain
statistical machine translation
lexical cohesion


Effective Utterance Classification with Unsupervised Phonotactic Models

Abstract: This paper describes a method for utterance classification that does not require manual transcription of training data. The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription. In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier. The classification accuracy of the method is evaluated on three different spoken language system domains.

utterance classification
transcription
unsupervised training


Implicit Discourse Relation Recognition by Selecting Typical Training Examples

Abstract: Implicit discourse relation recognition is a challenging task in the natural language processing field, but important to many applications such as question answering, summarizat ion and so on. Previous research used either art ificially created implicit discourse relat ions with connectives removed from explicit relations or annotated implicit relat ions as training data to detect the possible implicit relations, and do not further discern which examples are fit to be training data. This paper is the first time to apply a d ifferent typical/atypical perspective to select the most suitable discourse relation examples as training data. To differentiate typical and atypical examples for each discourse relation, a novel single centroid clustering algorithm is proposed. With this typical/atypical distinction, we aim to recognize those easily identified discourse relations more precisely so as to promote the performance of the implicit relation recognition. The experimental results verify that the proposed new method outperforms the state -of-the-art methods.

discourse relation
implicit
augment training data


Identifying Non-Explicit Citing Sentences for Citation-Based Summarization.

Abstract: Identifying background (context) information in scientific articles can help scholars understand major contributions in their research area more easily. In this paper, we propose a general framework based on probabilistic inference to extract such context information from scientific papers. We model the sentences in an article and their lexical similarities as a Markov Random Field tuned to detect the patterns that context data create, and employ a Belief Propagation mechanism to detect likely context sentences. We also address the problem of generating surveys of scientific papers. Our experiments show greater pyramid scores for surveys generated using such context information rather than citation sentences alone.

citation-based summarization
context sentence
non-explicit citation


Simple Unsupervised Grammar Induction from Raw Text with Cascaded Finite State Models

Abstract: We consider a new subproblem of unsupervised parsing from raw text, unsupervised partial parsing---the unsupervised version of text chunking. We show that addressing this task directly, using probabilistic finite-state methods, produces better results than relying on the local predictions of a current best unsu-pervised parser, Seginer's (2007) CCL. These finite-state models are combined in a cascade to produce more general (full-sentence) constituent structures; doing so outperforms CCL by a wide margin in unlabeled PARSEVAL scores for English, German and Chinese. Finally, we address the use of phrasal punctuation as a heuristic indicator of phrasal boundaries, both in our system and in CCL.

grammar induction
raw text
syntax-dependent model


Graph-based Local Coherence Modeling

Abstract: We propose a computationally efficient graph-based approach for local coherence modeling. We evaluate our system on three tasks: sentence ordering, summary coherence rating and readability assessment. The performance is comparable to entity grid based approaches though these rely on a computationally expensive training phase and face data sparsity problems.

local coherence modeling
sentence ordering
entity grid model


A New Approach to Animacy Detection

Abstract: Animacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, co-reference resolution, semantic role labeling, and others. Prior work treated animacy as a word-level property, and has developed statistical classifiers to classify words as either animate or inanimate. We discuss why this approach to the problem is ill-posed, and present a new approach based on classifying the animacy of co-reference chains. We show that simple voting approaches to inferring the animacy of a chain from its constituent words perform relatively poorly, and then present a hybrid system merging supervised machine learning (ML) and a small number of hand-built rules to compute the animacy of referring expressions and co-reference chains. This method achieves state of the art performance. The supervised ML component leverages features such as word embeddings over referring expressions, parts of speech, and grammatical and semantic roles. The rules take into consideration parts of speech and the hypernymy structure encoded in WordNet. The system achieves an F1 of 0.88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0.75 for classifying the animacy of coreference chains themselves. We release our training and test dataset, which includes 142 texts (all narratives) comprising 156,154 words, 34,698 referring expressions, and 10,941 co-reference chains. We test the method on a subset of the OntoNotes dataset, showing using manual sampling that animacy classification is 90% +/- 2% accurate for coreference chains, and 92% +/- 1% for referring expressions. The data also contains 46 folktales, which present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, trees that talk). We show that our system is able to detect the animacy of these unusual referents with an F1 of 0.95.

animacy detection
hand-built rule
word embedding


Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction

Abstract: Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available.

word-level prediction
morphologically-rich language
character-aware nlms


Improved Source-Channel Models for Chinese Word Segmentation

Abstract: This paper presents a Chinese word segmentation system that uses improved source-channel models of Chinese sentence generation. Chinese words are defined as one of the following four types: lexicon words, morphologically derived words, factoids, and named entities. Our system provides a unified approach to the four fundamental features of word-level Chinese language processing: (1) word segmentation, (2) morphological analysis, (3) factoid detection, and (4) named entity recognition. The performance of the system is evaluated on a manually annotated test set, and is also compared with several state-of-the-art systems, taking into account the fact that the definition of Chinese words often varies from system to system.

source-channel model
chinese word segmentation
word-based generative model


Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference

Abstract: This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. Secondly, we adopt factorization layers for efficient and expressive compression of alignment vectors into scalar features, which are then used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving competitive performance on all. A lightweight parameterization of our model also enjoys a 3 times reduction in parameter size compared to the existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining competitive performance. Additionally, visual analysis shows that our propagated features are highly interpretable.

natural language inference
alignment pair
factorization layer


MPC: A Multi-Party Chat Corpus for Modeling Social Phenomena in Discourse

Abstract: In this paper, we describe our experience with collecting and creating an annotated corpus of multi-party online conversations in a chat-room environment. This effort is part of a larger project to develop computational models of social phenomena such as agenda control, influence, and leadership in on-line interactions. Such models will help capturing the dialogue dynamics that are essential for developing, among others, realistic human-machine dialogue systems, including autonomous virtual chat agents. In this paper we describe data collection method used and the characteristics of the initial dataset of English chat. We have devised a multi-tiered collection process in which the subjects start from simple, free-flowing conversations and progress towards more complex and structured interactions. In this paper, we report on the first two stages of this process, which were recently completed. The third, large-scale collection effort is currently being conducted. All English dialogue has been annotated at four levels: communication links, dialogue acts, local topics and meso-topics. Some details of these annotations will be discussed later in this paper, although a full description is impossible within the scope of this article.


Overview of the IWSLT evaluation campaign

Abstract: This paper gives an overview of the evaluation campaign results of the IWSLT04 1 workshop, which is organized by the C-STAR 2 consortium to investigate novel speech translation technologies and their evaluation. The objectives of this workshop is to provide a framework for the applicability validation of existing machine translation evaluation methodologies to evaluate speech translation technologies. The work-shop also strives to ﬁnd new directions in how to improve current methods.

iwslt evaluation campaign
ted talk
translation quality


Learning Text Similarity with Siamese Recurrent Networks

Abstract: This paper presents a deep architecture for learning a similarity metric on variable-length character sequences. The model combines a stack of character-level bidirectional LSTM’s with a Siamese architecture. It learns to project variable-length strings into a ﬁxed-dimensional embedding space by using only information about the similarity between pairs of strings. This model is applied to the task of job title normalization based on a manually annotated taxonomy. A small data set is incrementally expanded and augmented with new sources of variance. The model learns a representation that is selective to differences in the input that reﬂect semantic differences (e.g., “Java developer” vs. “HR manager”) but also invariant to non-semantic string differences (e.g., “Java de-veloper” vs. “Java programmer”).

similarity metric
siamese architecture
job title normalization


Characterizing Departures from Linearity in Word Translation

Abstract: We investigate the behavior of maps learned by machine translation methods. The maps translate words by projecting between word embedding spaces of different languages. We locally approximate these maps using linear maps, and find that they vary across the word embedding space. This demonstrates that the underlying maps are non-linear. Importantly, we show that the locally linear maps vary by an amount that is tightly correlated with the distance between the neighborhoods on which they are trained. Our results can be used to test non-linear methods, and to drive the design of more accurate maps for word translation.

linearity
word translation
behavior
assumption
local region


Argumentation Quality Assessment: Theory vs. Practice

Abstract: Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches. This paper studies to what extent the views match empirically. We find that most observations on quality phrased spontaneously are in fact adequately represented by theory. Even more, relative comparisons of arguments in practice correlate with absolute quality ratings based on theory. Our results clarify how the two views can learn from each other.

argument quality
quality dimension
reasoning


Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks

Abstract: Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the cross-lingual links in multilingual KGs. In this paper, we propose a novel approach for cross-lingual KG alignment via graph convolutional networks (GCNs). Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space. Entity alignments are discovered based on the distances between entities in the embedding space. Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments. In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.

pre-aligned entity
graph encoder
low-dimensional vector space


The Social Impact of Natural Language Processing

Abstract: Medical sciences have long since established an ethics code for experiments, to minimize the risk of harm to subjects. Natural language processing (NLP) used to involve mostly anonymous corpora, with the goal of enriching linguistic analysis, and was therefore unlikely to raise ethical concerns. As NLP becomes increasingly wide-spread and uses more data from social media, however, the situation has changed: the outcome of NLP experiments and applications can now have a direct effect on individual users’ lives. Until now, the discourse on this topic in the ﬁeld has not followed the technological development, while public discourse was often focused on exaggerated dangers. This position paper tries to take back the initiative and start a discussion. We identify a number of social implications of NLP and discuss their ethical signiﬁcance, as well as ways to address them.


De-Mixing Sentiment from Code-Mixed Text

Abstract: Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today's multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54% accuracy and 0.827 F1 score - on a benchmark dataset.

code-mixed text
sentiment analysis
dual encoder network
collective
orthographic feature


Physical Causality of Action Verbs in Grounded Language Understanding

Abstract: Linguistics studies have shown that action verbs often denote some Change of State (CoS) as the result of an action. However, the causality of action verbs and its potential connection with the physical world has not been systematically explored. To address this limitation, this paper presents a study on physical causality of action verbs and their implied changes in the physical world. We ﬁrst conducted a crowd-sourcing experiment and identiﬁed eighteen categories of physical causality for action verbs. For a subset of these categories, we then deﬁned a set of detectors that detect the corresponding change from visual perception of the physical environment. We further incorporated physical causality modeling and state detection in grounded language understanding. Our empirical studies have demonstrated the effectiveness of causality modeling in grounding language to perception.

action verb
language understanding
change
perception
physical causality


Málrómur: A Manually Verified Corpus of Recorded Icelandic Speech

Abstract: This paper describes the Málrómur corpus, an open, manually verified, Icelandic speech corpus. The recordings were collected in 2011–2012 by Reykjavik University and the Icelandic Center for Language Technology in cooperation with Google. 152 hours of speech were recorded from 563 participants. The recordings were subsequently manually inspected by evaluators listening to all the segments, determining whether any given segment contains the utterance the participant was supposed to read, and nothing else. Out of 127,286 recorded segments 108,568 were approved and 18,718 deemed unsatisfactory.


IMHO Fine-Tuning Improves Claim Detection

Abstract: Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine-tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.

claim detection
language model
reddit corpus
opinion
imho


Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation

Abstract: This paper presents the results of the WMT10 and MetricsMATR10 shared tasks, which included a translation task, a system combination task, and an evaluation task. We conducted a large-scale manual evaluation of 104 machine translation systems and 41 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 26 metrics. This year we also investigated increasing the number of human judgments by hiring non-expert annotators through Amazon's Mechanical Turk.

statistical machine translation
wmt10
evaluation task


Weak Supervision for Learning Discourse Structure

Abstract: This paper provides a detailed comparison of a data programming approach with (i) off-the-shelf, state-of-the-art deep learning architectures that optimize their representations (BERT) and (ii) handcrafted-feature approaches previously used in the discourse analysis literature. We compare these approaches on the task of learning discourse structure for multi-party dialogue. The data programming paradigm offered by the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the “generative step” into probability distributions of the class labels given the data. We show that on our task the generative model outperforms both deep learning architectures as well as more traditional ML approaches when learning discourse structure—it even outperforms the combination of deep learning methods and hand-crafted features. We also implement several strategies for “decoding” our generative model output in order to improve our results. We conclude that weak supervision methods hold great promise as a means for creating and improving data sets for discourse structure.

discourse structure
learning method
weak supervision


Combining Argument Mining Techniques

Abstract: In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human reasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.

argument structure
discourse indicator
connection


Towards a Resource for Lexical Semantics: A Large German Corpus with Extensive Semantic Annotation

Abstract: We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large-scale acquisition of word-semantic information, e.g. the construction of domain-independent lexica. The backbone of the annotation are semantic roles in the frame semantics paradigm. We report experiences and evaluate the annotated data from the first project stage. On this basis, we discuss the problems of vagueness and ambiguity in semantic annotation.

german corpus
semantic role
salsa project


Multi-Document Person Name Resolution

Abstract: Multi-document person name resolution focuses on the problem of determining if two instances with the same name and from different documents refer to the same individual. We present a two-step approach in which a Maximum Entropy model is trained to give the probability that two names refer to the same individual. We then apply a modified agglomerative clustering technique to partition the instances according to their referents.

person name resolution
maximum entropy model
mention


Improved Word Sense Disambiguation with Enhanced Sense Representations

Abstract: Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre-trained language models and short dictionary definitions (or glosses) of the different word senses. While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses. In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms. We show that incorporating such additional information boosts the performance on WSD. With the proposed enhancements, our system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task, surpassing all previous published scores on this benchmark dataset.


Refining Word Embeddings for Sentiment Analysis

Abstract: Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically fail to capture sufficient sentiment information. This may result in words with similar vector representations having an opposite sentiment polarity (e.g., good and bad), thus degrading sentiment analysis performance. Therefore, this study proposes a word vector refinement model that can be applied to any pre-trained word vectors (e.g., Word2vec and GloVe). The refinement model is based on adjusting the vector representations of words such that they can be closer to both semantically and sentimentally similar words and further away from sentimentally dissimilar words. Experimental results show that the proposed method can improve conventional word embeddings and outperform previously proposed sentiment embeddings for both binary and fine-grained classification on Stanford Sentiment Treebank (SST).

sentiment analysis
syntactic information
pre-trained word vector


The IMAGACT Visual Ontology. An Extendable Multilingual Infrastructure for the representation of lexical encoding of Action

Abstract: Action verbs have many meanings, covering actions in different ontological types. Moreover, each language categorizes action in its own way. One verb can refer to many different actions and one action can be identified by more than one verb. The range of variations within and across languages is largely unknown, causing trouble for natural language processing tasks. IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech corpora, which makes use of the universal language of images to identify the different action types extended by verbs referring to action in English, Italian, Chinese and Spanish. This paper presents the infrastructure and the various linguistic information the user can derive from it. IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages. Because the action concepts are represented with videos, extension into new languages beyond those presently implemented in IMAGACT is done using competence-based judgments by mother-tongue informants without intense lexicographic work involving underdetermined semantic description

imagact visual ontology
lexical encoding
corpus-based ontology
action concept


The Revised Arabic PropBank

Abstract: The revised Arabic PropBank (APB) reflects a number of changes to the data and the process of PropBanking. Several changes stem from Treebank revisions. An automatic process was put in place to map existing annotation to the new trees. We have revised the original 493 Frame Files from the Pilot APB and added 1462 new files for a total of 1955 Frame Files with 2446 framesets. In addition to a heightened attention to sense distinctions this cycle includes a greater attempt to address complicated predicates such as light verb constructions and multi-word expressions. New tools facilitate the data tagging and also simplify frame creation.

arabic propbank
apb
semantic role labeling


Speechalator: Two-Way Speech-to-Speech Translation in Your Hand

Abstract: This demonstration involves two-way automatic speech-to-speech translation on a consumer off-the-shelf PDA. This work was done as part of the DARPA-funded Babylon project, investigating better speech-to-speech translation systems for communication in the field. The development of the Speechalator software-based translation system required addressing a number of hard issues, including a new language for the team (Egyptian Arabic), close integration on a small device, computational efficiency on a limited platform, and scalable coverage for the domain.


Towards Creating Precision Grammars from Interlinear Glossed Text: Inferring Large-Scale Typological Properties

Abstract: We propose to bring together two kinds of linguistic resources—interlinear glossed text (IGT) and a language-independent precision grammar resource—to automatically create precision grammars in the context of language documentation. This paper takes the first steps in that direction by extracting major-constituent word order and case system properties from IGT for a diverse sample of languages.

precision grammar
large-scale typological property
igt


Black-box integration of heterogeneous bilingual resources into an interactive translation system

Abstract: The objective of interactive translation prediction (ITP) is to assist human translators in the translation of texts by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. In this paper, however, we propose a resource-agnostic approach in which the suggestions are obtained from any bilingual resource (a machine translation system, a translation memory, a bilingual dictionary, etc.) that provides targetlanguage equivalents for source-language segments. These bilingual resources are considered to be black boxes and do not need to be adapted to the peculiarities of the ITP system. Our evaluation shows that savings of up to 85% can be theoretically achieved in the number of keystrokes when using our novel approach. Preliminary user trials indicate that these benefits can be partly transferred to real-world computer-assisted translation interfaces.

bilingual resource
translator
interactive translation prediction


Dependency and relational structure in treebank annotation

Abstract: Among the variety of proposals currently making the dependency perspective on grammar more concrete, there are several treebanks whose annotation exploits some form of Relational Structure that we can consider a generalization of the fundamental idea of dependency at various degrees and with reference to different types of linguistic knowledge. The paper describes the Relational Structure as the common underlying representation of treebanks which is motivated by both theoretical and task-dependent considerations. Then it presents a system for the annotation of the Relational Structure in treebanks, called Augmented Relational Structure, which allows for a systematic annotation of various components of linguistic knowledge crucial in several tasks. Finally, it shows a dependency-based annotation for an Italian treebank, i.e. the Turin University Treebank, that implements the Augmented Relational Structure.

relational structure
non-projective dependency tree
syntax


Generalized Algorithms for Constructing Statistical Language Models

Abstract: Recent text and speech processing applications such as speech mining raise new and more general problems related to the construction of language models. We present and describe in detail several new and efficient algorithms to address these more general problems and report experimental results demonstrating their usefulness. We give an algorithm for computing efficiently the expected counts of any sequence in a word lattice output by a speech recognizer or any arbitrary weighted automaton; describe a new technique for creating exact representations of n-gram language models by weighted automata whose size is practical for offline use even for a vocabulary size of about 500,000 words and an n-gram order n = 6; and present a simple and more general technique for constructing class-based language models that allows each class to represent an arbitrary weighted automaton. An efficient implementation of our algorithms and techniques has been incorporated in a general software library for language modeling, the GRM Library, that includes many other text and grammar processing functionalities.


Decentralized Entity-Level Modeling for Coreference Resolution

Abstract: Efficiently incorporating entity-level information is a challenge for coreference resolution systems due to the difficulty of exact inference over partitions. We describe an end-to-end discriminative probabilistic model for coreference that, along with standard pairwise features, enforces structural agreement constraints between specified properties of coreferent mentions. This model can be represented as a factor graph for each document that admits efficient inference via belief propagation. We show that our method can use entity-level information to outperform a basic pairwise system.

coreference resolution
probabilistic model
factor graph


Error-Driven Analysis of Challenges in Coreference Resolution

Abstract: Coreference resolution metrics quantify errors but do not analyze them. Here, we consider an automated method of categorizing errors in the output of a coreference system into intuitive underlying error types. Using this tool, we first compare the error distributions across a large set of systems, then analyze common errors across the top ten systems, empirically characterizing the major unsolved challenges of the coreference resolution task.

coreference system
state-of-the-art system
reference


Identifying Literary Texts with Bigrams

Abstract: We study perceptions of literariness in a set of contemporary Dutch novels. Experiments with machine learning models show that it is possible to automatically distinguish novels that are seen as highly literary from those that are seen as less literary, using surprisingly simple textual features. The most discriminating features of our classification model indicate that genre might be a confounding factor, but a regression model shows that we can also explain variation between highly literary novels from less literary ones within genre.


Robust Neural Machine Translation with Joint Textual and Phonetic Embedding

Abstract: Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises. Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.

neural machine translation
phonetic information
training corpus


Intrinsic Evaluation of Summarization Datasets

Abstract: High quality data forms the bedrock for building meaningful statistical models in NLP. Consequently, data quality must be evaluated either during dataset construction or *post hoc*. Almost all popular summarization datasets are drawn from natural sources and do not come with inherent quality assurance guarantees. In spite of this, data quality has gone largely unquestioned for many of these recent datasets. We perform the first large-scale evaluation of summarization datasets by introducing 5 intrinsic metrics and applying them to 10 popular datasets. We find that data usage in recent summarization research is sometimes inconsistent with the underlying properties of the data. Further, we discover that our metrics can serve the additional purpose of being inexpensive heuristics for detecting generically low quality examples.

summarization dataset
purpose
intrinsic evaluation


Time-Efficient Creation of an Accurate Sentence Fusion Corpus

Abstract: Sentence fusion enables summarization and question-answering systems to produce output by combining fully formed phrases from different sentences. Yet there is little data that can be used to develop and evaluate fusion techniques. In this paper, we present a methodology for collecting fusions of similar sentence pairs using Amazon's Mechanical Turk, selecting the input pairs in a semi-automated fashion. We evaluate the results using a novel technique for automatically selecting a representative sentence from multiple responses. Our approach allows for rapid construction of a high accuracy fusion corpus.


Variational Knowledge Graph Reasoning

Abstract: Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (Diva) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, Diva is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.

knowledge graph
latent variable
path
diva
reinforcement learning


Gamification for Word Sense Labeling

Abstract: Obtaining gold standard data for word sense disambiguation is important but costly. We show how it can be done using a “Game with a Purpose” (GWAP) called Wordrobe. This game consists of a large set of multiple-choice questions on word senses generated from the Groningen Meaning Bank. The players need to answer these questions, scoring points depending on the agreement with fellow players. The working assumption is that the right sense for a word can be determined by the answers given by the players. To evaluate our method, we gold-standard tagged a portion of the data that was also used in the GWAP. A comparison yielded promising results, ranging from a precision of 0.88 and recall of 0.83 for relative majority agreement, to a precision of 0.98 and recall of 0.35 for questions that were answered unanimously.

word sense disambiguation
multiple-choice question
groningen meaning bank


Collective Entity Resolution with Multi-Focal Attention

Abstract: Entity resolution is the task of linking each mention of an entity in text to the corresponding record in a knowledge base (KB). Coherence models for entity resolution encourage all referring expressions in a document to resolve to entities that are related in the KB. We explore attention-like mechanisms for coherence, where the evidence for each candidate is based on a small set of strong relations, rather than relations to all other entities in the document. The rationale is that document-wide support may simply not exist for non-salient entities, or entities not densely connected in the KB. Our proposed sys-tem outperforms state-of-the-art systems on the CoNLL 2003, TAC KBP 2010, 2011 and 2012 tasks.

coherence
other entity
state-of-the-art system


Fast and Accurate Arc Filtering for Dependency Parsing

Abstract: We propose a series of learned arc filters to speed up graph-based dependency parsing. A cascade of filters identify implausible head-modifier pairs, with time complexity that is first linear, and then quadratic in the length of the sentence. The linear filters reliably predict, in context, words that are roots or leaves of dependency trees, and words that are likely to have heads on their left or right. We use this information to quickly prune arcs from the dependency graph. More than 78% of total arcs are pruned while retaining 99.5% of the true dependencies. These filters improve the speed of two state-of-the-art dependency parsers, with low overhead and negligible loss in accuracy.


Quantifying sentence complexity based on eye-tracking measures

Abstract: Eye-tracking reading times have been attested to reflect cognitive processes underlying sentence comprehension. However, the use of reading times in NLP applications is an underexplored area of research. In this initial work we build an automatic system to assess sentence complexity using automatically predicted eye-tracking reading time measures and demonstrate the efficacy of these reading times for a well known NLP task, namely, readability assessment. We use a machine learning model and a set of features known to be significant predictors of reading times in order to learn per-word reading times from a corpus of English text having reading times of human readers. Subsequently, we use the model to predict reading times for novel text in the context of the aforementioned task. A model based only on reading times gave competitive results compared to the systems that use extensive syntactic features to compute linguistic complexity. Our work, to the best of our knowledge, is the first study to show that automatically predicted reading times can successfully model the difficulty of a text and can be deployed in practical text processing applications.

sentence complexity
token-level
gaze behaviour


Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification

Abstract: Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. However, one of the remaining challenges is to model long texts in document-level sentiment classification under a recurrent architecture because of the deficiency of the memory unit. To address this problem, we present a Cached Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic information in long texts. CLSTM introduces a cache mechanism, which divides memory into several groups with different forgetting rates and thus enables the network to keep sentiment information better within a recurrent unit. The proposed CLSTM outperforms the state-of-the-art models on three publicly available document-level sentiment analysis datasets.

short-term memory
document-level sentiment classification
semantic information


KeyGames: A Game Theoretic Approach to Automatic Keyphrase Extraction

Abstract: In this paper, we introduce two advancements in the automatic keyphrase extraction (AKE) space - KeyGames and pke+. KeyGames is an unsupervised AKE framework that employs the concept of evolutionary game theory and consistent labelling problem to ensure consistent classification of candidates into keyphrase and non-keyphrase. Pke+ is a python based pipeline built on top of the existing pke library to standardize various AKE steps, namely candidate extraction and evaluation, to ensure truly systematic and comparable performance analysis of AKE models. In the experiments section, we compare the performance of KeyGames across three publicly available datasets (Inspec 2001, SemEval 2010, DUC 2001) against the results quoted by the existing state-of-the-art models as well as their performance when reproduced using pke+. The results show that KeyGames outperforms most of the state-of-the-art systems while generalizing better on input documents with different domains and length. Further, pke+'s pre-processing brings out improvement in several other system's quoted performance as well.

automatic keyphrase extraction
game theory
candidate


Statistical QA - Classifier vs. Re-ranker: What's the difference?

Abstract: In this paper, we show that we can obtain a good baseline performance for Question Answering (QA) by using only 4 simple features. Using these features, we contrast two approaches used for a Maximum Entropy based QA system. We view the QA problem as a classification problem and as a re-ranking problem. Our results indicate that the QA system viewed as a re-ranker clearly outperforms the QA system used as a classifier. Both systems are trained using the same data.

re-ranker
question answering
classification problem


Sparse Information Extraction: Unsupervised Language Models to the Rescue

Abstract: Even in a massive corpus such as the Web, a substantial fraction of extractions appear infrequently. This paper shows how to assess the correctness of sparse extractions by utilizing unsupervised language models. The REALM system, which combines HMMbased and n-gram-based language models, ranks candidate extractions by the likelihood that they are correct. Our experiments show that REALM reduces extraction error by 39%, on average, when compared with previous work. Because REALM pre-computes language models based on its corpus and does not require any hand-tagged seeds, it is far more scalable than approaches that learn models for each individual relation from handtagged data. Thus, REALM is ideally suited for open information extraction where the relations of interest are not specified in advance and their number is potentially vast.

sparse information extraction
candidate relation
hmm


Classifying Speech Acts using Verbal Response Modes

Abstract: The driving vision for our work is to provide intelligent, automated assistance to users in understanding the status of their email conversations. Our approach is to create tools that enable the detection and connection of speech acts across email messages. We thus require a mechanism for tagging email utterances with some indication of their dialogic function. However, existing dialog act taxonomies as used in computational linguistics tend to be too taskor application-specific for the wide range of acts we find represented in email conversation. The Verbal Response Modes (VRM) taxonomy of speech acts, widely applied for discourse analysis in linguistics and psychology, is distinguished from other speech act taxonomies by its construction from crosscutting principles of classification, which ensure universal applicability across any domain of discourse. The taxonomy categorises on two dimensions, characterised as literal meaning and pragmatic meaning. In this paper, we describe a statistical classifier that automatically identifies the literal meaning category of utterances using the VRM classification. We achieve an accuracy of 60.8% using linguistic features derived from VRM’s human annotation guidelines. Accuracy is improved to 79.8% using additional features.


Stochastic Lexicalized Inversion Transduction Grammar for Alignment

Abstract: We present a version of Inversion Transduction Grammar where rule probabilities are lexicalized throughout the synchronous parse tree, along with pruning techniques for efficient training. Alignment results improve over unlexicalized ITG on short sentences for which full EM is feasible, but pruning seems to have a negative impact on longer sentences.

efficient training
itg
word alignment
tic-tac-toe pruning


Metaphor Detection using Ensembles of Bidirectional Recurrent Neural Networks

Abstract: In this paper we present our results from the Second Shared Task on Metaphor Detection, hosted by the Second Workshop on Figurative Language Processing. We use an ensemble of RNN models with bidirectional LSTMs and bidirectional attention mechanisms. Some of the models were trained on all parts of speech. Each of the other models was trained on one of four categories for parts of speech: “nouns”, “verbs”, “adverbs/adjectives”, or “other”. The models were combined into voting pools and the voting pools were combined using the logical “OR” operator.

bidirectional attention mechanism
metaphor detection
bi-lstms


Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning

Abstract: Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent's actions can bring about myriad changes in the scene. Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional video captioning. Unlike images, actions in videos are also inherently linked to social aspects such as intentions (why the action is taking place), effects (what changes due to the action), and attributes that describe the agent. Thus for video understanding, such as when captioning videos or when answering questions about videos, one must have an understanding of these commonsense aspects. We present the first work on generating commonsense captions directly from videos, to describe latent aspects such as intentions, effects, and attributes. We present a new dataset “Video-to-Commonsense (V2C)” that contains ~9k videos of human agents performing various actions, annotated with 3 types of commonsense descriptions. Additionally we explore the use of open-ended video-based commonsense question answering (V2C-QA) as a way to enrich our captions. Both the generation task and the QA task can be used to enrich video captions.

commonsense description
video caption
video2commonsense


The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns

Abstract: The problem addressed in this paper is to predict a user's numeric rating in a product review from the text of the review. Unigram and n-gram representations of text are common choices in opinion mining. However, unigrams cannot capture important expressions like "could have been better", which are essential for prediction models of ratings. N-grams of words, on the other hand, capture such phrases, but typically occur too sparsely in the training set and thus fail to yield robust predictors. This paper overcomes the limitations of these two models, by introducing a novel kind of bag-of-opinions representation, where an opinion, within a review, consists of three components: a root word, a set of modifier words from the same sentence, and one or more negation words. Each opinion is assigned a numeric score which is learned, by ridge regression, from a large, domain-independent corpus of reviews. For the actual test case of a domain-dependent review, the review's rating is predicted by aggregating the scores of all opinions in the review and combining it with a domain-dependent unigram model. The paper presents a constrained ridge regression algorithm for learning opinion scores. Experiments show that the bag-of-opinions method outperforms prior state-of-the-art techniques for review rating prediction.

review rating prediction
bag-of-opinion representation
root word


MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER

Abstract: Named Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. The paper first proposes a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids problems such as word order change and entity span determination. With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages. These augmented data enable the language model based NER models to generalize better with both the language-specific features from the target-language synthetic data and the language-independent features from multilingual synthetic data. An extensive set of experiments were conducted to demonstrate encouraging cross-lingual transfer performance of the new research on a wide variety of target languages.

cross-lingual ner
low-resource language
data augmentation method


Aligning words using matrix factorisation

Abstract: Aligning words from sentences which are mutual translations is an important problem in different settings, such as bilingual terminology extraction, Machine Translation, or projection of linguistic features. Here, we view word alignment as matrix factorisation. In order to produce proper alignments, we show that factors must satisfy a number of constraints such as orthogonality. We then propose an algorithm for orthogonal non-negative matrix factorisation, based on a probabilistic model of the alignment data, and apply it to word alignment. This is illustrated on a French-English alignment task from the Hansard.

matrix factorisation
machine translation
aer


Web-Scale Features for Full-Scale Parsing

Abstract: Counts from large corpora (like the web) can be powerful syntactic cues. Past work has used web counts to help resolve isolated ambiguities, such as binary noun-verb PP attachments and noun compound bracketings. In this work, we first present a method for generating web count features that address the full range of syntactic attachments. These features encode both surface evidence of lexical affinities as well as paraphrase-based cues to syntactic structure. We then integrate our features into full-scale dependency and constituent parsers. We show relative error reductions of 7.0% over the second-order dependency parser of McDonald and Pereira (2006), 9.2% over the constituent parser of Petrov et al. (2006), and 3.4% over a non-local constituent reranker.

web-scale feature
conjunction
unlabeled data


Interpretable Entity Representations through Large-Scale Typing

Abstract: In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret. In this paper, we present an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box. Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types, indicating the confidence of a typing model's decision that the entity belongs to the corresponding type. We obtain these representations using a fine-grained entity typing model, trained either on supervised ultra-fine entity typing data (Choi et al. 2018) or distantly-supervised examples from Wikipedia. On entity probing tasks involving recognizing entity identity, our embeddings used in parameter-free downstream models achieve competitive performance with ELMo- and BERT-based embeddings in trained models. We also show that it is possible to reduce the size of our type set in a learning-based way for particular domains. Finally, we show that these embeddings can be post-hoc modified through a small number of rules to incorporate domain knowledge and improve performance.

entity representation
probability
ultra-fine entity


Dual Decomposition Inference for Graphical Models over Strings

Abstract: We investigate dual decomposition for joint MAP inference of many strings. Given an arbitrary graphical model, we decompose it into small acyclic sub-models, whose MAP configurations can be found by finite-state composition and dynamic programming. We force the solutions of these subproblems to agree on overlapping variables, by tuning Lagrange multipliers for an adaptively expanding set of variable-lengthn-gram count features. This is the first inference method for arbitrary graphical models over strings that does not require approximations such as random sampling, message simplification, or a bound on string length. Provided that the inference method terminates, it gives a certificate of global optimality (though MAP inference in our setting is undecidable in general). On our global phonological inference problems, it always terminates, and achieves more accurate results than max-product and sum-product loopy belief propagation.

string
dual decomposition technique
instance-level constraint


Improving Grammatical Error Correction with Machine Translation Pairs

Abstract: We propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models (e.g., Chinese to English) of different qualities (i.e., poor and good). The poor translation model can resemble the ESL (English as a second language) learner and tends to generate translations of low quality in terms of fluency and grammaticality, while the good translation model generally generates fluent and grammatically correct translations. With the pair of translation models, we can generate unlimited numbers of poor to good English sentence pairs from text in the source language (e.g., Chinese) of the translators. Our approach can generate various error-corrected patterns and nicely complement the other data synthesis approaches for GEC. Experimental results demonstrate the data generated by our approach can effectively help a GEC model to improve the performance and achieve the state-of-the-art single-model performance in BEA-19 and CoNLL-14 benchmark datasets.

grammatical error correction
sentence pair
different quality


Question Answering Based on Semantic Structures

Abstract: The ability to answer complex questions posed in Natural Language depends on (1) the depth of the available semantic representations and (2) the inferential mechanisms they support. In this paper we describe a QA architecture where questions are analyzed and candidate answers generated by 1) identifying predicate argument structures and semantic frames from the input and 2) performing structured probabilistic inference using the extracted relations in the context of a domain and scenario model. A novel aspect of our system is a scalable and expressive representation of actions and events based on Coordinated Probabilistic Relational Models (CPRM). In this paper we report on the ability of the implemented system to perform several forms of probabilistic and temporal inferences to extract answers to complex questions. The results indicate enhanced accuracy over current state-of-the-art Q/A systems.

action
question answering
information extraction


Story Cloze Task: UW NLP System

Abstract: This paper describes University of Washington NLP's submission for the Linking Models of Lexical, Sentential and Discourse-level Semantics (LSDSem 2017) shared task—the Story Cloze Task. Our system is a linear classifier with a variety of features, including both the scores of a neural language model and style features. We report 75.2% accuracy on the task. A further discussion of our results can be found in Schwartz et al. (2017).

language model
story cloze task
stylistic feature


Scalable Deep Linguistic Processing: Mind the Lexical Gap

Abstract: Coverage has been a constant thorn in the side of deployed deep linguistic processing applications, largely because of the difficulty in constructing, maintaining and domaintuning the complex lexicons that they rely on. This paper reviews various strands of research on deep lexical acquisition (DLA), i.e. the (semi-)automatic creation of linguistically-rich language resources, particularly from the viewpoint of DLA for precision grammars.

deep lexical acquisition
dla
precision grammar


Minimal Dependency Length in Realization Ranking

Abstract: Comprehension and corpus studies have found that the tendency to minimize dependency length has a strong influence on constituent ordering choices. In this paper, we investigate dependency length minimization in the context of discriminative realization ranking, focusing on its potential to eliminate egregious ordering errors as well as better match the distributional characteristics of sentence orderings in news text. We find that with a state-of-the-art, comprehensive realization ranking model, dependency length minimization yields statistically significant improvements in BLEU scores and significantly reduces the number of heavy/light ordering errors. Through distributional analyses, we also show that with simpler ranking models, dependency length minimization can go overboard, too often sacrificing canonical word order to shorten dependencies, while richer models manage to better counterbalance the dependency length minimization preference against (sometimes) competing canonical word order preferences.

dependency length
realization
ranking model


Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models

Abstract: Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations. We propose Conpono, an inter-sentence objective for pretraining language models that models discourse coherence and the distance between sentences. Given an anchor sentence, our model is trained to predict the text k sentences away using a sampled-softmax objective where the candidates consist of neighboring sentences and sentences randomly sampled from the corpus. On the discourse representation benchmark DiscoEval, our model improves over the previous state-of-the-art by up to 13% and on average 4% absolute across 7 tasks. Our model is the same size as BERT-Base, but outperforms the much larger BERT-Large model and other more recent approaches that incorporate discourse. We also show that Conpono yields gains of 2%-6% absolute even for tasks that do not explicitly evaluate discourse: textual entailment (RTE), common sense reasoning (COPA) and reading comprehension (ReCoRD).

discourse-level representation
inter-sentence objective
contrastive learning