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NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension.

, , , and . SemEval@ACL/IJCNLP, page 827-832. Association for Computational Linguistics, (2021)

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CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation., , and . COLING, page 5205-5216. International Committee on Computational Linguistics, (2022)NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension., , , and . SemEval@ACL/IJCNLP, page 827-832. Association for Computational Linguistics, (2021)PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation., , , and . ACL (1), page 13433-13447. Association for Computational Linguistics, (2023)CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation., , , , , , and . ACL (Findings), page 3123-3137. Association for Computational Linguistics, (2023)Towards a Deep Understanding of Multilingual End-to-End Speech Translation., , , , and . EMNLP (Findings), page 14332-14348. Association for Computational Linguistics, (2023)CCSRD: Content-Centric Speech Representation Disentanglement Learning for End-to-End Speech Translation., , , , and . EMNLP (Findings), page 5920-5932. Association for Computational Linguistics, (2023)Multilingual Neural Machine Transliteration with Adaptive Segmentation Schemes., , and . IALP, page 494-499. IEEE, (2022)