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First the Worst: Finding Better Gender Translations During Beam Search.

, , and . ACL (Findings), page 3814-3823. Association for Computational Linguistics, (2022)

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Inference-only sub-character decomposition improves translation of unseen logographic characters., , and . WAT@AAC/IJCNLPL, page 170-177. Association for Computational Linguistics, (2020)Using Context in Neural Machine Translation Training Objectives., , and . ACL, page 7764-7770. Association for Computational Linguistics, (2020)Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation., , , and . AMTA (1), page 208-216. Association for Machine Translation in the Americas, (2018)Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It., , and . CoRR, (2020)Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey.. J. Artif. Intell. Res., (2022)The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing., , , , and . Ethics Inf. Technol., 23 (3): 419-433 (2021)Domain Adaptation for Neural Machine Translation.. EAMT, page 9-10. European Association for Machine Translation, (2022)An Operation Sequence Model for Explainable Neural Machine Translation., , and . BlackboxNLP@EMNLP, page 175-186. Association for Computational Linguistics, (2018)Multi-representation ensembles and delayed SGD updates improve syntax-based NMT., , , and . ACL (2), page 319-325. Association for Computational Linguistics, (2018)SGNMT - A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies., , , and . EMNLP (System Demonstrations), page 25-30. Association for Computational Linguistics, (2017)