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Viewing 1-10 of 35 papers
  • 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… more
  • 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… more
  • 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… more
  • 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… more
  • Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization

    Ansong Ni, Matt Gardner, Pradeep DasigiEMNLP2021 Question Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information from which the reasoning model can derive an answer. The retrieval model is typically trained to maximize the… more
  • Paired Examples as Indirect Supervision in Latent Decision Models

    Nitish Gupta, Sameer Singh, Matt Gardner and Dan RothEMNLP2021 Compositional, structured models are appealing because they explicitly decompose problems and provide interpretable intermediate outputs that give confidence that the model is not simply latching onto data artifacts. Learning these models is challenging… more
  • A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

    Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, Matt GardnerNAACL2021 Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that… more
  • Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq

    Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, IV RobertL.Logan, Ana Marasović, Z. NieEMNLP • Demo2020 High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designing a user-friendly annotation interface; (2) training enough annotators… more
  • IIRC: A Dataset of Incomplete Information Reading Comprehension Questions

    James Ferguson, Matt Gardner. Hannaneh Hajishirzi, Tushar Khot, Pradeep DasigiEMNLP2020 Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a… more
  • Improving Compositional Generalization in Semantic Parsing

    Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, Jonathan BerantFindings of EMNLP2020 Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes to new structures built of components observed during training, has sparked… more
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