Papers

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Viewing 211-220 of 275 papers
  • Evaluating Question Answering Evaluation

    Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt GardnerEMNLP • MRQA Workshop2019 As the complexity of question answering (QA) datasets evolve, moving away from restricted formats like span extraction and multiple-choice (MC) to free-form answer generation, it is imperative to understand how well current metrics perform in evaluating QA…
  • On Making Reading Comprehension More Comprehensive

    Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon MinEMNLP • MRQA Workshop2019 Machine reading comprehension, the task of evaluating a machine’s ability to comprehend a passage of text, has seen a surge in popularity in recent years. There are many datasets that are targeted at reading comprehension, and many systems that perform as…
  • ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

    Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, Matt GardnerEMNLP • MRQA Workshop2019 Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple…
  • AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

    Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matthew Gardner, Sameer SinghEMNLP2019 Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation methods ameliorate this opacity by providing explanations for specific model…
  • Do NLP Models Know Numbers? Probing Numeracy in Embeddings

    Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt GardnerEMNLP2019 The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens---they embed them as distributed vectors. Is this enough to capture…
  • Efficient Navigation with Language Pre-training and Stochastic Sampling

    Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah Smith, Yejin ChoiEMNLP2019 Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments. In this paper, we report two simple but highly…
  • Global Reasoning over Database Structures for Text-to-SQL Parsing

    Ben Bogin, Matt Gardner, Jonathan BerantEMNLP2019 State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database…
  • Knowledge Enhanced Contextual Word Representations

    Matthew E. Peters, Mark Neumann, Robert L. Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. SmithEMNLP2019 Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple…
  • Low-Resource Parsing with Crosslingual Contextualized Representations

    Phoebe Mulcaire, Jungo Kasai, Noah A. SmithCoNLL2019 Despite advances in dependency parsing, languages with small treebanks still present challenges. We assess recent approaches to multilingual contextual word representations (CWRs), and compare them for crosslingual transfer from a language with a large…
  • On the Limits of Learning to Actively Learn Semantic Representations

    Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar and Jonathan BerantCoNLL2019
    Best Paper Honorable Mention
    One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively…