Papers

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Viewing 11-20 of 571 papers
  • BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief

    Nora Kassner, Oyvind Tafjord, H. Schutze, P. ClarkEMNLP2021 Although pretrained language models (PTLMs) have been shown to contain significant amounts of world knowledge, they can still produce inconsistent answers to questions when probed, even after using specialized training techniques to reduce inconsistency. As a…
  • CLIPScore: A Reference-free Evaluation Metric for Image Captioning

    Jack Hessel, Ariel Holtzman, Maxwell Forbes, R. L. Bras, Yejin ChoiEMNLP2021 Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in stark contrast to the reference-free manner in which humans assess caption quality. In this…
  • Competency Problems: On Finding and Removing Artifacts in Language Data

    Matt Gardner, William Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh and Noah A. SmithEMNLP2021 Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this…
  • Contrastive Explanations for Model Interpretability

    Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, Yoav GoldbergEMNLP2021 Contrastive explanations clarify why an event occurred in contrast to another. They are more inherently intuitive to humans to both produce and comprehend. We propose a methodology to produce contrastive explanations for classification models by modifying the…
  • Cross-Document Language Modeling

    Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E. Peters, Arie Cattan, Ido DaganFindings of EMNLP2021 We introduce a new pretraining approach for language models that are geared to support multi-document NLP tasks. Our crossdocument language model (CD-LM) improves masked language modeling for these tasks with two key ideas. First, we pretrain with multiple…
  • Documenting the English Colossal Clean Crawled Corpus

    Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Matt GardnerEMNLP2021 As language models are trained on ever more text, researchers are turning to some of the largest corpora available. Unlike most other types of datasets in NLP, large unlabeled text corpora are often presented with minimal documentation, and best practices for…
  • Explaining Answers with Entailment Trees

    Bhavana Dalvi, Peter A. Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, Peter ClarkEMNLP2021 Our goal, in the context of open-domain textual question-answering (QA), is to explain answers by not just listing supporting textual evidence (“rationales”), but also showing how such evidence leads to the answer in a systematic way. If this could be done…
  • Generative Context Pair Selection for Multi-hop Question Answering

    Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng, Ben Athiwaratkun, Bing Xiang, Matt Gardner, Sameer SinghEMNLP2021 Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question. However, crowdsourced datasets often capture only a slice of the underlying task distribution, which can induce…
  • GooAQ: Open Question Answering with Diverse Answer Types

    Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hanna Hajishirzi, Chris Callison-BurchFindings of EMNLP2021 While day-to-day questions come with a variety of answer types, the current questionanswering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GOOAQ, a large-scale dataset with a variety of answer…
  • Learning with Instance Bundles for Reading Comprehension

    Dheeru Dua, Pradeep Dasigi, Sameer Singh and Matt GardnerEMNLP2021 When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related questions and their corresponding answers are not independent, and leveraging these…