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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…pyBART: Evidence-based Syntactic Transformations for IE
Aryeh Tiktinsky, Yoav Goldberg, Reut TsarfatyACL • 2020 Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect…QuASE: Question-Answer Driven Sentence Encoding.
Hangfeng He, Qiang Ning, Dan RothACL • 2020 Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, {\em can we use QAMR (Michael et al., 2017) to…Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models
Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker ACL • 2020 We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release HIPPOCORPUS, a dataset of 7,000 stories about imagined and recalled…S2ORC: The Semantic Scholar Open Research Corpus
Kyle Lo, Lucy Lu Wang, Mark E Neumann, Rodney Michael Kinney, Daniel S. Weld ACL • 2020 We introduce S2ORC, a large contextual citation graph of English-language academic papers from multiple scientific domains; the corpus consists of 81.1M papers, 380.5M citation edges, and associated paper metadata. We provide structured full text for 8.1M…SciREX: A Challenge Dataset for Document-Level Information Extraction
Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, Iz BeltagyACL • 2020 Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at…Social Bias Frames: Reasoning about Social and Power Implications of Language
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin ChoiACL • 2020Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but all the implied meanings that frame people's judgements about others. For example, given a…WeCNLP Best PaperSPECTER: Document-level Representation Learning using Citation-informed Transformers
Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. WeldACL • 2020 Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards tokenand sentence-level training objectives…Stolen Probability: A Structural Weakness of Neural Language Models
David Demeter, Gregory Kimmel, Doug DowneyACL • 2020 Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word vectors in a high-dimensional embedding space. The dot-product…Syntactic Search by Example
Micah Shlain, Hillel Taub-Tabib, Shoval Sadde, Yoav GoldbergACL • 2020 We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs. In contrast to previous attempts to this effect, we introduce a light-weight query language that does not require the…