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CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan BerantNAACL • 2019 When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background…DiscoFuse: A Large-Scale Dataset for Discourse-based Sentence Fusion
Mor Geva, Eric Malmi, Idan Szpektor, Jonathan BerantNAACL • 2019 Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically…DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt GardnerNAACL-HLT • 2019 Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be done. We…Evaluating Text GANs as Language Models
Guy Tevet, Gavriel Habib, Vered Shwartz, Jonathan BerantNAACL • 2019 Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of “exposure bias”. However, A major hurdle for understanding the potential of GANs for text…Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets
Nelson F. Liu, Roy Schwartz, Noah SmithNAACL • 2019 Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared to the original benchmark, it is unclear what particular…Iterative Search for Weakly Supervised Semantic Parsing
Pradeep Dasigi, Matt Gardner, Shikhar Murty, Luke Zettlemoyer, Ed HovyNAACL • 2019 Training semantic parsers from question-answer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We…Linguistic Knowledge and Transferability of Contextual Representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew Peters, Noah A. SmithNAACL • 2019 Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture…Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
Hila Gonen, Yoav GoldbergNAACL • 2019 Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious…MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
ida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh HajishirziNAACL • 2019 We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver by learning to map problems to their operation programs. Due to annotation challenges, current datasets in this domain have been either relatively small…Polyglot Contextual Representations Improve Crosslingual Transfer
Phoebe Mulcaire, Jungo Kasai, Noah A. SmithNAACL • 2019 We introduce a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word representations with those of multilingual representation…