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Stop Measuring Calibration When Humans Disagree.

, , , and . EMNLP, page 1892-1915. Association for Computational Linguistics, (2022)

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Question Answering by Reasoning Across Documents with Graph Convolutional Networks., , and . NAACL-HLT (1), page 2306-2317. Association for Computational Linguistics, (2019)Examining the Relationship between Preordering and Word Order Freedom in Machine Translation., , , and . WMT, page 118-130. The Association for Computer Linguistics, (2016)Predicting Machine Translation Adequacy., , , and . MTSummit, (2011)Latent Variable Model for Multi-modal Translation., , and . ACL (1), page 6392-6405. Association for Computational Linguistics, (2019)Latent Visual Cues for Neural Machine Translation., , and . CoRR, (2018)Editing Factual Knowledge in Language Models., , and . EMNLP (1), page 6491-6506. Association for Computational Linguistics, (2021)Sparse Communication via Mixed Distributions., , , and . ICLR, OpenReview.net, (2022)What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability., , , , and . EMNLP, page 14349-14371. Association for Computational Linguistics, (2023)Deep Generative Model for Joint Alignment and Word Representation., , and . NAACL-HLT, page 1011-1023. Association for Computational Linguistics, (2018)Cross-lingual Sentence Compression for Subtitles., , and . EAMT, page 103-110. European Association for Machine Translation, (2012)