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Pretrained Language Models for Sequential Sentence Classification
Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Daniel S. WeldEMNLP • 2019 As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task…QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions
Oyvind Tafjord, Matt Gardner, Kevin Lin, Peter ClarkEMNLP • 2019 We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., "A sunscreen with a higher SPF protects the skin longer.", twinned with 3864 crowdsourced…Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, Noah A. Smith, Matt GardnerEMNLP • 2019 Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate the ability of models to…RNN Architecture Learning with Sparse Regularization
Jesse Dodge, Roy Schwartz, Hao Peng, Noah A. SmithEMNLP • 2019 Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient NLP models…SciBERT: A Pretrained Language Model for Scientific Text
Iz Beltagy, Kyle Lo, Arman CohanEMNLP • 2019 Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific…Show Your Work: Improved Reporting of Experimental Results
Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. SmithEMNLP • 2019 Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone…Social IQA: Commonsense Reasoning about Social Interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, Yejin ChoiEMNLP • 2019 We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations (e.g., Q: "Jordan…SpanBERT: Improving Pre-training by Representing and Predicting Spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, Omer LevyEMNLP • 2019 We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to…Topics to Avoid: Demoting Latent Confounds in Text Classification
Sachin Kumar, Shuly Wintner, Noah A. Smith, Yulia TsvetkovEMNLP • 2019 Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect…Transfer Learning Between Related Tasks Using Expected Label Proportions
Matan Ben Noach, Yoav GoldbergEMNLP • 2019 Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained…