Papers

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Viewing 201-210 of 293 papers
  • Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

    Ana Marasović, Chandra Bhagavatula, J. Park, Ronan Le Bras, Noah A. Smith, Yejin ChoiFindings of EMNLP2020 Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study…
  • Parsing with Multilingual BERT, a Small Treebank, and a Small Corpus

    Ethan C. Chau, Lucy H. Lin, Noah A. SmithFindings of EMNLP2020 Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This presents a challenge for language varieties unfamiliar to these…
  • Plug and Play Autoencoders for Conditional Text Generation

    Florian Mai, Nikolaos Pappas, I. Montero, Noah A. SmithEMNLP2020 Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space…
  • RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

    Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, Noah A. SmithFindings of EMNLP2020 Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the…
  • The Multilingual Amazon Reviews Corpus

    Phillip Keung, Y. Lu, Gyorgy Szarvas, Noah A. SmithEMNLP2020 We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between…
  • TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

    Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, Dan RothEMNLP2020 A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated. However, current machine reading comprehension benchmarks have…
  • Writing Strategies for Science Communication: Data and Computational Analysis

    Tal August, Lauren Kim, Katharina Reinecke, Noah A. SmithEMNLP2020 Communicating complex scientific ideas without misleading or overwhelming the public is challenging. While science communication guides exist, they rarely offer empirical evidence for how their strategies are used in practice. Writing trategies that can be…
  • Evaluating Models' Local Decision Boundaries via Contrast Sets

    M. Gardner, Y. Artzi, V. Basmova, J. Berant, B. Bogin, S. Chen, P. Dasigi, D. Dua, Y. Elazar, A. Gottumukkala, N. Gupta, H. Hajishirzi, G. Ilharco, D.Khashabi, K. Lin, J. Liu, N. F. Liu, P. Mulcaire, Q. Ning, S.Singh, N.A. Smith, S. Subramanian, et alFindings of EMNLP2020 Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on…
  • Break It Down: A Question Understanding Benchmark

    Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan BerantTACL2020 Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the…
  • CORD-19: The Covid-19 Open Research Dataset

    L. Lu Wang, K. Lo, Y. Chandrasekhar, R. Reas, J. Yang, D. Eide, K. Funk, R. Kinney, Z. Liu, W. Merrill, P. Mooney, D. Murdick, D. Rishi, J. Sheehan, Z. Shen, B. Stilson, A. D Wade, K. Wang, C. Wilhelm, B. Xie, D.Raymond, D. S Weld, O. Etzioni, S. KohlmeierACL • NLP-COVID2020 The Covid-19 Open Research Dataset (CORD-19) is a growing 1 resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich…