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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…Repurposing Entailment for Multi-Hop Question Answering Tasks
Harsh Trivedi, Heeyoung Kwon, Tushar Khot, Ashish Sabharwal, Niranjan BalasubramanianNAACL • 2019 Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which require reasoning with multiple sentences, it remains unclear how best to utilize…Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Amit Mor-Yosef, Ido Dagan, Yoav GoldbergNAACL • 2019 Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a…Structural Scaffolds for Citation Intent Classification in Scientific Publications
Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field CadyNAACL • 2019 Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose a multitask…