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TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, Dan RothEMNLP • 2020 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. SmithEMNLP • 2020 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 EMNLP • 2020 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 BerantTACL • 2020 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-COVID • 2020 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…A Formal Hierarchy of RNN Architectures
William. Merrill, Gail Garfinkel Weiss, Yoav Goldberg, Roy Schwartz, Noah A. Smith, Eran YahavACL • 2020 We develop a formal hierarchy of the expressive capacity of RNN architectures. The hierarchy is based on two formal properties: space complexity, which measures the RNN's memory, and rational recurrence, defined as whether the recurrent update can be…A Mixture of h-1 Heads is Better than h Heads
Hao Peng, Roy Schwartz, Dianqi Li, Noah A. SmithACL • 2020 Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks. Evidence has shown that they are overparameterized; attention heads can be pruned without significant performance loss. In this…Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. SmithACL • 2020 Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target…Improving Transformer Models by Reordering their Sublayers
Ofir Press, Noah A. Smith, Omer LevyACL • 2020 Multilayer transformer networks consist of interleaved self-attention and feedforward sublayers. Could ordering the sublayers in a different pattern lead to better performance? We generate randomly ordered transformers and train them with the language…Obtaining Faithful Interpretations from Compositional Neural Networks
Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, Matt Gardner ACL • 2020 Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However…