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There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training.

, , , , and . CoRR, (2020)

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DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages., , , , and . EMNLP (1), page 7079-7091. Association for Computational Linguistics, (2021)Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change., , , and . ACL (1), page 457-470. Association for Computational Linguistics, (2019)SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection., , , , and . SemEval@COLING, page 1-23. International Committee for Computational Linguistics, (2020)Analyzing Semantic Change through Lexical Replacements., , , and . CoRR, (2024)Strengthening the WiC: New Polysemy Dataset in Hindi and Lack of Cross Lingual Transfer., and . LREC/COLING, page 15341-15349. ELRA and ICCL, (2024)Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models., , and . EMNLP, page 1136-1145. Association for Computational Linguistics, (2017)A bottom up approach to category mapping and meaning change., , , and . NetWordS, volume 1347 of CEUR Workshop Proceedings, page 66-70. CEUR-WS.org, (2015)Toward Sentiment Aware Semantic Change Analysis., and . EACL (Student Research Workshop), page 350-357. Association for Computational Linguistics, (2024)Integrating Cognitive Neuroscience Insights into NLP: A New Approach to Understanding Narrative Processing (Abstract)., , and . Text2Story@ECIR, volume 3671 of CEUR Workshop Proceedings, page 149. CEUR-WS.org, (2024)Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training., , , , and . EMNLP (1), page 8281-8291. Association for Computational Linguistics, (2020)