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On the Limits of Learning to Actively Learn Semantic Representations
Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar and Jonathan BerantCoNLL • 2019One 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…Best Paper Honorable MentionPaLM: A Hybrid Parser and Language Model
Hao Peng, Roy Schwartz, Noah A. SmithEMNLP • 2019 We present PaLM, a hybrid parser and neural language model. Building on an RNN language model, PaLM adds an attention layer over text spans in the left context. An unsupervised constituency parser can be derived from its attention weights, using a greedy…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…