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    • ACL 2019
      Elizabeth Clark, Asli Çelikyilmaz, Noah A. Smith
      For evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming. The most common automatic metrics, like BLEU and ROUGE, depend on exact word matching, an inflexible approach for measuring semantic…  (More)
    • ACL 2019
      Sofia Serrano, Noah A. Smith
      Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word…  (More)
    • ACL 2019
      Suchin Gururangan, Tam Dang, Dallas Card, Noah A. Smith
      We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream…  (More)
    • NAACL-HLT 2019
      Xinya Du, Bhavana Dalvi Mishra, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter Clark, Claire Cardie
      Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a recipe). This task is challenging as the world is changing throughout the text, and despite recent…  (More)
    • NAACL-HLT 2019
      Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner
      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 introduce a new English reading…  (More)
    • NAACL 2019
      Yonatan Bisk, Jan Buys, Karl Pichotta, Yejin Choi
    • NAACL 2019
      Pradeep Dasigi, Matt Gardner, Shikhar Murty, Luke Zettlemoyer, Ed Hovy
      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 propose a novel iterative training…  (More)
    • NAACL 2019
      ida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh Hajishirzi
      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 in scale or did not offer precise…  (More)
    • NAACL 2019
      Hila Gonen, Yoav Goldberg
      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 concern. Several recent works tackle…  (More)
    • NAACL 2019
      Nelson F. Liu, Roy Schwartz, Noah Smith
      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 weaknesses they reveal. For example, a…  (More)
    • Award Best Resource Paper
      NAACL 2019
      Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant
      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. To investigate question answering with…  (More)
    • NAACL 2019
      Amit Mor-Yosef, Ido Dagan, Yoav Goldberg
      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 single end-to-end differentiable system…  (More)
    • NAACL 2019
      Noa Yehezkel, Jacob Goldberger, Yoav Goldberg
      The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the set of aligned points is allowed to…  (More)
    • NAACL 2019
      Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field Cady
      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 approach to incorporate information in…  (More)
    • NAACL 2019
      Or Gorodissky, Yoav Chai, Yotam Gil, Jonathan Berant
      We show that a neural network can learn to imitate the optimization process performed by white-box attack in a much more efficient manner. We train a black-box attack through this imitation process and show our attack is 19x-39x faster than the white-box attack and also that we can perform a black…  (More)
    • NAACL 2019
      Iz Beltagy, Kyle Lo, Waleed Ammar
      In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant…  (More)
    • NAACL 2019
      Yi Luan, Dave Wadden, Luheng He, Mari Ostendorf, Hannaneh Hajishirzi
      We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by selecting the most confident entity spans and linking these nodes with confidence-weighted relation types…  (More)
    • NAACL 2019
      Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi
      Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts…  (More)
    • NAACL 2019
      Harsh Trivedi, Heeyoung Kwon, Tushar Khot, Ashish Sabharwal, Niranjan Balasubramanian
      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 entailment models pre-trained on large…  (More)
    • NAACL 2019
      Phoebe Mulcaire, Jungo Kasai, Noah A. Smith
      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 learning. We produce language models…  (More)