mtybilly/Instruct2DS huggingface dataset:
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections
Authors: Zhisong Zhang, Taylor Berg-Kirkpatrick, Graham Neubig Junxian He
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available. In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly. The parameters of source model and target model are softly shared through a regularized log likelihood objective. An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect cross-lingual word embedding input. We evaluate our method on two syntactic tasks: part-of-speech (POS) tagging and dependency parsing. On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages. On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2% absolute improvement on POS tagging and 8.3% absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models.
Unsupervised Joint Training of Bilingual Word Embeddings
Authors: Atsushi Fujita Benjamin Marie
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: State-of-the-art methods for unsupervised bilingual word embeddings (BWE) train a mapping function that maps pre-trained monolingual word embeddings into a bilingual space. Despite its remarkable results, unsupervised mapping is also well-known to be limited by the original dissimilarity between the word embedding spaces to be mapped. In this work, we propose a new approach that trains unsupervised BWE jointly on synthetic parallel data generated through unsupervised machine translation. We demonstrate that existing algorithms that jointly train BWE are very robust to noisy training data and show that unsupervised BWE jointly trained significantly outperform unsupervised mapped BWE in several cross-lingual NLP tasks.
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
Authors: Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel Matthew Le
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We consider the task of inferring “is-a” relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and to correct wrong extractions. Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
Authors: Yuxian Meng, Xiaofei Sun, Qinghong Han, Arianna Yuan, Jiwei Li Xiaoya Li
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored. In this paper, we ask the fundamental question of whether Chinese word segmentation (CWS) is necessary for deep learning-based Chinese Natural Language Processing. We benchmark neural word-based models which rely on word segmentation against neural char-based models which do not involve word segmentation in four end-to-end NLP benchmark tasks: language modeling, machine translation, sentence matching/paraphrase and text classification. Through direct comparisons between these two types of models, we find that char-based models consistently outperform word-based models. Based on these observations, we conduct comprehensive experiments to study why word-based models underperform char-based models in these deep learning-based NLP tasks. We show that it is because word-based models are more vulnerable to data sparsity and the presence of out-of-vocabulary (OOV) words, and thus more prone to overfitting. We hope this paper could encourage researchers in the community to rethink the necessity of word segmentation in deep learning-based Chinese Natural Language Processing.
Towards Understanding Linear Word Analogies
Authors: David Duvenaud, Graeme Hirst Kawin Ethayarajh
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: A surprising property of word vectors is that word analogies can often be solved with vector arithmetic. However, it is unclear why arithmetic operators correspond to non-linear embedding models such as skip-gram with negative sampling (SGNS). We provide a formal explanation of this phenomenon without making the strong assumptions that past theories have made about the vector space and word distribution. Our theory has several implications. Past work has conjectured that linear substructures exist in vector spaces because relations can be represented as ratios; we prove that this holds for SGNS. We provide novel justification for the addition of SGNS word vectors by showing that it automatically down-weights the more frequent word, as weighting schemes do ad hoc. Lastly, we offer an information theoretic interpretation of Euclidean distance in vector spaces, justifying its use in capturing word dissimilarity.
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
Authors: Dima Puzyrev, Alexander Panchenko, Pawan Goyal, Chris Biemann, Animesh Mukherjee Abhik Jana
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincaré similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements. Further, we publicly release our Poincaré embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.
Robust Representation Learning of Biomedical Names
Authors: Aixin Sun, Yi Tay Minh C. Phan
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Biomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering downstream applications inefficacious and/or potentially unreliable. This paper proposes a new framework for learning robust representations of biomedical names and terms. The idea behind our approach is to consider and encode contextual meaning, conceptual meaning, and the similarity between synonyms during the representation learning process. Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks. Moreover, our proposed method is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications.
Relational Word Embeddings
Authors: Luis Espinosa Anke, Steven Schockaert Jose Camacho-Collados
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding. Such strategies may not be optimal, however, as they are limited by the coverage of available resources and conflate similarity with other forms of relatedness. As an alternative, in this paper we propose to encode relational knowledge in a separate word embedding, which is aimed to be complementary to a given standard word embedding. This relational word embedding is still learned from co-occurrence statistics, and can thus be used even when no external knowledge base is available. Our analysis shows that relational word vectors do indeed capture information that is complementary to what is encoded in standard word embeddings.
Unraveling Antonym`s Word Vectors through a Siamese-like Network
Authors: Dina Wonsever Mathias Etcheverry
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Discriminating antonyms and synonyms is an important NLP task that has the difficulty that both, antonyms and synonyms, contains similar distributional information. Consequently, pairs of antonyms and synonyms may have similar word vectors. We present an approach to unravel antonymy and synonymy from word vectors based on a siamese network inspired approach. The model consists of a two-phase training of the same base network: a pre-training phase according to a siamese model supervised by synonyms and a training phase on antonyms through a siamese-like model that supports the antitransitivity present in antonymy. The approach makes use of the claim that the antonyms in common of a word tend to be synonyms. We show that our approach outperforms distributional and pattern-based approaches, relaying on a simple feed forward network as base network of the training phases.
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Authors: Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar Shikhar Vashishth
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some attempts at utilizing syntactic context of a word, such methods result in an explosion of the vocabulary size. In this paper, we overcome this problem by proposing SynGCN, a flexible Graph Convolution based method for learning word embeddings. SynGCN utilizes the dependency context of a word without increasing the vocabulary size. Word embeddings learned by SynGCN outperform existing methods on various intrinsic and extrinsic tasks and provide an advantage when used with ELMo. We also propose SemGCN, an effective framework for incorporating diverse semantic knowledge for further enhancing learned word representations. We make the source code of both models available to encourage reproducible research.
Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors
Authors: Steven Schockaert Shoaib Jameel
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Word embedding models typically learn two types of vectors: target word vectors and context word vectors. These vectors are normally learned such that they are predictive of some word co-occurrence statistic, but they are otherwise unconstrained. However, the words from a given language can be organized in various natural groupings, such as syntactic word classes (e.g. nouns, adjectives, verbs) and semantic themes (e.g. sports, politics, sentiment). Our hypothesis in this paper is that embedding models can be improved by explicitly imposing a cluster structure on the set of context word vectors. To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises-Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors. We show that this results in word vectors which are qualitatively different from those obtained with existing word embedding models. We furthermore show that our embedding model can also be used to learn high-quality document representations.
Delta Embedding Learning
Authors: Ji Wu, Dejing Dou Xiao Zhang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Unsupervised word embeddings have become a popular approach of word representation in NLP tasks. However there are limitations to the semantics represented by unsupervised embeddings, and inadequate fine-tuning of embeddings can lead to suboptimal performance. We propose a novel learning technique called Delta Embedding Learning, which can be applied to general NLP tasks to improve performance by optimized tuning of the word embeddings. A structured regularization is applied to the embeddings to ensure they are tuned in an incremental way. As a result, the tuned word embeddings become better word representations by absorbing semantic information from supervision without “forgetting.” We apply the method to various NLP tasks and see a consistent improvement in performance. Evaluation also confirms the tuned word embeddings have better semantic properties.
Annotation and Automatic Classification of Aspectual Categories
Authors: Helena Prepens, Will Roberts Markus Egg
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We present the first annotated resource for the aspectual classification of German verb tokens in their clausal context. We use aspectual features compatible with the plurality of aspectual classifications in previous work and treat aspectual ambiguity systematically. We evaluate our corpus by using it to train supervised classifiers to automatically assign aspectual categories to verbs in context, permitting favourable comparisons to previous work.
Putting Words in Context: LSTM Language Models and Lexical Ambiguity
Authors: Kristina Gulordava, Gemma Boleda Laura Aina
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, representing the contextually relevant information is not trivial. We investigate how an LSTM language model deals with lexical ambiguity in English, designing a method to probe its hidden representations for lexical and contextual information about words. We find that both types of information are represented to a large extent, but also that there is room for improvement for contextual information.
Making Fast Graph-based Algorithms with Graph Metric Embeddings
Authors: Mohammad Dorgham, Oleksiy Oliynyk, Chris Biemann, Alexander Panchenko Andrey Kutuzov
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Graph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks.
Embedding Imputation with Grounded Language Information
Authors: Chenguang Zhu, Vin Sachidananda, Eric Darve Ziyi Yang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical problem in language processing. Embedding imputation involves learning representations for rare or unseen words during the training of an embedding model, often in a post-hoc manner. In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph. This is in contrast to existing approaches which typically make use of vector space properties or subword information. We propose an online method to construct a graph from grounded information and design an algorithm to map from the resulting graphical structure to the space of the pre-trained embeddings. Finally, we evaluate our approach on a range of rare and unseen word tasks across various domains and show that our model can learn better representations. For example, on the Card-660 task our method improves Pearson`s and Spearman`s correlation coefficients upon the state-of-the-art by 11% and 17.8% respectively using GloVe embeddings.
The Effectiveness of Simple Hybrid Systems for Hypernym Discovery
Authors: Nizar Habash William Held
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Hypernymy modeling has largely been separated according to two paradigms, pattern-based methods and distributional methods. However, recent works utilizing a mix of these strategies have yielded state-of-the-art results. This paper evaluates the contribution of both paradigms to hybrid success by evaluating the benefits of hybrid treatment of baseline models from each paradigm. Even with a simple methodology for each individual system, utilizing a hybrid approach establishes new state-of-the-art results on two domain-specific English hypernym discovery tasks and outperforms all non-hybrid approaches in a general English hypernym discovery task.
BERT-based Lexical Substitution
Authors: Tao Ge, Ke Xu, Furu Wei, Ming Zhou Wangchunshu Zhou
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Previous studies on lexical substitution tend to obtain substitute candidates by finding the target word`s synonyms from lexical resources (e.g., WordNet) and then rank the candidates based on its contexts. These approaches have two limitations: (1) They are likely to overlook good substitute candidates that are not the synonyms of the target words in the lexical resources; (2) They fail to take into account the substitution`s influence on the global context of the sentence. To address these issues, we propose an end-to-end BERT-based lexical substitution approach which can propose and validate substitute candidates without using any annotated data or manually curated resources. Our approach first applies dropout to the target word`s embedding for partially masking the word, allowing BERT to take balanced consideration of the target word`s semantics and contexts for proposing substitute candidates, and then validates the candidates based on their substitution`s influence on the global contextualized representation of the sentence. Experiments show our approach performs well in both proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks.
Exploring Numeracy in Word Embeddings
Authors: Abhilasha Ravichander, Carolyn Rose, Eduard Hovy Aakanksha Naik
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Word embeddings are now pervasive across NLP subfields as the de-facto method of forming text representataions. In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding. Numbers are ubiquitous and frequently appear in text. Inspired by cognitive studies on how humans perceive numbers, we develop an analysis framework to test how well word embeddings capture two essential properties of numbers: magnitude (e.g. 3\ensuremath<4) and numeration (e.g. 3=three). Our experiments reveal that most models capture an approximate notion of magnitude, but are inadequate at capturing numeration. We hope that our observations provide a starting point for the development of methods which better capture numeracy in NLP systems.
HighRES: Highlight-based Reference-less Evaluation of Summarization
Authors: Shashi Narayan, Andreas Vlachos Hardy Hardy
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches. However, manual evaluation of the system generated summaries is inconsistent due to the difficulty the task poses to human non-expert readers. To address this issue, we propose a novel approach for manual evaluation, Highlight-based Reference-less Evaluation of Summarization (HighRES), in which summaries are assessed by multiple annotators against the source document via manually highlighted salient content in the latter. Thus summary assessment on the source document by human judges is facilitated, while the highlights can be used for evaluating multiple systems. To validate our approach we employ crowd-workers to augment with highlights a recently proposed dataset and compare two state-of-the-art systems. We demonstrate that HighRES improves inter-annotator agreement in comparison to using the source document directly, while they help emphasize differences among systems that would be ignored under other evaluation approaches.
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
Authors: Zichao Li, Mehdi Rezagholizadeh, Jackie Chi Kit Cheung Yue Dong
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. These methods learn to simplify sentences as a byproduct of the fact that they are trained on complex-simple sentence pairs. By contrast, our neural programmer-interpreter is directly trained to predict explicit edit operations on targeted parts of the input sentence, resembling the way that humans perform simplification and revision. Our model outperforms previous state-of-the-art neural sentence simplification models (without external knowledge) by large margins on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, +1.41 Newsela), and is judged by humans to produce overall better and simpler output sentences.
Decomposable Neural Paraphrase Generation
Authors: Xin Jiang, Lifeng Shang, Qun Liu Zichao Li
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to state-of-the-art neural models, and significantly better performance when adapting to a new domain.
Transforming Complex Sentences into a Semantic Hierarchy
Authors: Matthias Cetto, André Freitas, Siegfried Handschuh Christina Niklaus
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
Authors: Ellie Pavlick, Tal Linzen R. Thomas McCoy
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area.
Zero-Shot Entity Linking by Reading Entity Descriptions
Authors: Ming-Wei Chang, Kenton Lee, Kristina Toutanova, Jacob Devlin, Honglak Lee Lajanugen Logeswaran
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly specialized domains, and so no metadata or alias tables are assumed. In this setting, entities are only identified by text descriptions, and models must rely strictly on language understanding to resolve the new entities. First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities. Second, we propose a simple and effective adaptive pre-training strategy, which we term domain-adaptive pre-training (DAP), to address the domain shift problem associated with linking unseen entities in a new domain. We present experiments on a new dataset that we construct for this task and show that DAP improves over strong pre-training baselines, including BERT. The data and code are available at r̆lhttps://github.com/lajanugen/zeshel.
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
Authors: Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, Kenneth Kwok Joey Tianyi Zhou
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model.
Scalable Syntax-Aware Language Models Using Knowledge Distillation
Authors: Chris Dyer, Laura Rimell, Stephen Clark, Phil Blunsom Adhiguna Kuncoro
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential language models. However, their computational complexity renders scaling difficult, and it remains an open question whether structural biases are still necessary when sequential models have access to ever larger amounts of training data. To answer this question, we introduce an efficient knowledge distillation (KD) technique that transfers knowledge from a syntactic language model trained on a small corpus to an LSTM language model, hence enabling the LSTM to develop a more structurally sensitive representation of the larger training data it learns from. On targeted syntactic evaluations, we find that, while sequential LSTMs perform much better than previously reported, our proposed technique substantially improves on this baseline, yielding a new state of the art. Our findings and analysis affirm the importance of structural biases, even in models that learn from large amounts of data.
An Imitation Learning Approach to Unsupervised Parsing
Authors: Lili Mou, Frank Keller Bowen Li
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the learned trees often do not match actual syntax trees well. Shen et al. (2018) propose a structured attention mechanism for language modeling (PRPN), which induces better syntactic structures but relies on ad hoc heuristics. Also, their model lacks interpretability as it is not grounded in parsing actions. In our work, we propose an imitation learning approach to unsupervised parsing, where we transfer the syntactic knowledge induced by PRPN to a Tree-LSTM model with discrete parsing actions. Its policy is then refined by Gumbel-Softmax training towards a semantically oriented objective. We evaluate our approach on the All Natural Language Inference dataset and show that it achieves a new state of the art in terms of parsing F-score, outperforming our base models, including PRPN.
Women`s Syntactic Resilience and Men`s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing
Authors: Carmen Banea, Dirk Hovy, Rada Mihalcea Aparna Garimella
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences. To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles' authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women. Further analyses reveal numerous part-of-speech tags and syntactic relations whose prediction performances benefit from the prevalence of a specific gender in the training data. The results underscore the importance of accounting for gendered differences in syntactic tasks, and outline future venues for developing more accurate taggers and parsers. We release our data to the research community.
Multilingual Constituency Parsing with Self-Attention and Pre-Training
Authors: Steven Cao, Dan Klein Nikita Kitaev
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1).
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction
Authors: Hinrich Schütze Mengjie Zhao
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world`s languages. We evaluate our method on Parallel Bible Corpus+ (PBC+), a parallel corpus of 1593 languages. The key idea is to use Byte Pair Encodings (BPEs) as basic units for multilingual embeddings. Through zero-shot transfer from English sentiment, we learn a seed lexicon for each language in the domain of PBC+. Through domain adaptation, we then generalize the domain-specific lexicon to a general one. We show -- across typologically diverse languages in PBC+ -- good quality of seed and general-domain sentiment lexicons by intrinsic and extrinsic and by automatic and human evaluation. We make freely available our code, seed sentiment lexicons for all 1593 languages and induced general-domain sentiment lexicons for 200 languages.
Tree Communication Models for Sentiment Analysis
Authors: Yue Zhang Yuan Zhang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents. In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text
Authors: Niloy Ganguly, Soumen Chakrabarti Bidisha Samanta
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Multilingual writers and speakers often alternate between two languages in a single discourse. This practice is called “code-switching”. Existing sentiment detection methods are usually trained on sentiment-labeled monolingual text. Manually labeled code-switched text, especially involving minority languages, is extremely rare. Consequently, the best monolingual methods perform relatively poorly on code-switched text. We present an effective technique for synthesizing labeled code-switched text from labeled monolingual text, which is relatively readily available. The idea is to replace carefully selected subtrees of constituency parses of sentences in the resource-rich language with suitable token spans selected from automatic translations to the resource-poor language. By augmenting the scarce labeled code-switched text with plentiful synthetic labeled code-switched text, we achieve significant improvements in sentiment labeling accuracy (1.5%, 5.11% 7.20%) for three different language pairs (English-Hindi, English-Spanish and English-Bengali). The improvement is even significant in hatespeech detection whereby we achieve a 4% improvement using only synthetic code-switched data (6% with data augmentation).
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction
Authors: Sujian Li, Fangzhao Wu, Xing Xie, Houfeng Wang Dehong Ma
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem. However, sequence labeling based methods cannot make full use of the overall meaning of the whole sentence and have the limitation in processing dependencies between labels. To tackle these problems, we first explore to formalize ATE as a sequence-to-sequence (Seq2Seq) learning task where the source sequence and target sequence are composed of words and labels respectively. At the same time, to make Seq2Seq learning suit to ATE where labels correspond to words one by one, we design the gated unit networks to incorporate corresponding word representation into the decoder, and position-aware attention to pay more attention to the adjacent words of a target word. The experimental results on two datasets show that Seq2Seq learning is effective in ATE accompanied with our proposed gated unit networks and position-aware attention mechanism.
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network
Authors: Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou Jingjing Wang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.
ELI5: Long Form Question Answering
Authors: Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, Michael Auli Angela Fan
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We introduce the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum “Explain Like I`m Five” (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement.
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
Authors: Seonhoon Kim, Nojun Kwak Daesik Kim
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of the TQA dataset. First, solving the TQA problems requires to comprehend multi-modal contexts in complicated input data. To tackle this issue of extracting knowledge features from long text lessons and merging them with visual features, we establish a context graph from texts and images, and propose a new module f-GCN based on graph convolutional networks (GCN). Second, scientific terms are not spread over the chapters and subjects are split in the TQA dataset. To overcome this so called \textquoteleftout-of-domain' issue, before learning QA problems, we introduce a novel self-supervised open-set learning process without any annotations. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.
Generating Question Relevant Captions to Aid Visual Question Answering
Authors: Zeyuan Hu, Raymond Mooney Jialin Wu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to better VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is trained using an existing caption dataset by automatically determining question-relevant captions using an online gradient-based method. Experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g. 68.4% in the Test-standard set using a single model) by simultaneously generating question-relevant captions.
Multi-grained Attention with Object-level Grounding for Visual Question Answering
Authors: Jianhui Huang, Yuqing Guo, Min Qiao, Yong Zhu Pingping Huang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this paper proposes a multi-grained attention method. It learns explicit word-object correspondence by two types of word-level attention complementary to the sentence-image association. Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with state-of-the-art models. And the visualized attention maps demonstrate that addition of object-level groundings leads to a better understanding of the images and locates the attended objects more precisely.
Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering
Authors: Barbara Plank, Raquel Fernández, Raffaella Bernardi Claudio Greco
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA). Motivated by evidence from psycholinguistics, we devise a set of linguistically-informed VQA tasks, which differ by the types of questions involved (Wh-questions and polar questions). We test what impact task difficulty has on continual learning, and whether the order in which a child acquires question types facilitates computational models. Our results show that dramatic forgetting is at play and that task difficulty and order matter. Two well-known current continual learning methods mitigate the problem only to a limiting degree.