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Clinical Concept Extraction for Document-Level Coding.

, , , , and . BioNLP@ACL, page 261-272. Association for Computational Linguistics, (2019)

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Attentiveness to Answer Choices Doesn't Always Entail High QA Accuracy., , , , and . CoRR, (2023)The Unreasonable Effectiveness of Easy Training Data for Hard Tasks., , , and . CoRR, (2024)Measuring Association Between Labels and Free-Text Rationales., , and . EMNLP (1), page 10266-10284. Association for Computational Linguistics, (2021)Editing Common Sense in Transformers., , , , , , and . EMNLP, page 8214-8232. Association for Computational Linguistics, (2023)Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals., , , , , , , and . CoRR, (2023)Reframing Human-AI Collaboration for Generating Free-Text Explanations., , , , and . NAACL-HLT, page 632-658. Association for Computational Linguistics, (2022)Clinical Concept Extraction for Document-Level Coding., , , , and . BioNLP@ACL, page 261-272. Association for Computational Linguistics, (2019)Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy., , , , and . EMNLP, page 8392-8417. Association for Computational Linguistics, (2023)Teach Me to Explain: A Review of Datasets for Explainable NLP., and . CoRR, (2021)Learning to Faithfully Rationalize by Construction., , , and . ACL, page 4459-4473. Association for Computational Linguistics, (2020)