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Learning The Difference That Makes A Difference With Counterfactually-Augmented Data.

, , and . ICLR, OpenReview.net, (2020)

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Learning the Difference that Makes a Difference with Counterfactually-Augmented Data., , and . CoRR, (2019)Practical Benefits of Feature Feedback Under Distribution Shift., , , , and . BlackboxNLP@EMNLP, page 346-355. Association for Computational Linguistics, (2022)Explaining The Efficacy of Counterfactually-Augmented Data., , , and . CoRR, (2020)Learning The Difference That Makes A Difference With Counterfactually-Augmented Data., , and . ICLR, OpenReview.net, (2020)Explaining the Efficacy of Counterfactually Augmented Data., , , and . ICLR, OpenReview.net, (2021)Resolving the Human Subjects Status of Machine Learning's Crowdworkers., , and . CoRR, (2022)Practical Benefits of Feature Feedback Under Distribution Shift., , , , and . CoRR, (2021)Resolving the Human-subjects Status of Machine Learning's Crowdworkers: What ethical framework should govern the interaction of ML researchers and crowdworkers?, , and . ACM Queue, 21 (6): 101-127 (2024)On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study., , , and . ACL/IJCNLP (1), page 6618-6633. Association for Computational Linguistics, (2021)Dynabench: Rethinking Benchmarking in NLP., , , , , , , , , and 9 other author(s). NAACL-HLT, page 4110-4124. Association for Computational Linguistics, (2021)