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QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation.

, , , and . LREC/COLING, page 13371-13381. ELRA and ICCL, (2024)

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QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation., , , and . LREC/COLING, page 13371-13381. ELRA and ICCL, (2024)Identifying collocations using cross-lingual association measures., , , and . MWE@EACL, page 109-113. The Association for Computer Linguistics, (2014)Posterior Differential Regularization with f-divergence for Improving Model Robustness., , , , and . NAACL-HLT, page 1078-1089. Association for Computational Linguistics, (2021)ALICE++: Adversarial Training for Robust and Effective Temporal Reasoning., , , and . PACLIC, page 373-382. Association for Computational Lingustics, (2021)Collocation or Free Combination? ― Applying Machine Translation Techniques to identify collocations in Japanese., , and . LREC, page 736-739. European Language Resources Association (ELRA), (2014)Automated Collocation Suggestion for Japanese Second Language Learners., , and . ACL (Student Research Workshop), page 52-58. The Association for Computer Linguistics, (2013)OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection., and . SemEval@NAACL, page 217-220. Association for Computational Linguistics, (2022)Collocation Assistant for Learners of Japanese as a Second Language., and . NLP-TEA@ACL/IJCNLP, page 20-25. Association for Computational Linguistics, (2015)OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models., , and . SMM4H@NAACL-HLT, page 123-125. Association for Computational Linguistics, (2021)Targeted Adversarial Training for Natural Language Understanding., , , , , and . NAACL-HLT, page 5385-5393. Association for Computational Linguistics, (2021)