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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)

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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)First the Worst: Finding Better Gender Translations During Beam Search., , and . ACL (Findings), page 3814-3823. Association for Computational Linguistics, (2022)Morph-to-word transduction for accurate and efficient automatic speech recognition and keyword search., , , , , and . ICASSP, page 5770-5774. IEEE, (2017)Domain Adaptive Inference for Neural Machine Translation., , , and . ACL (1), page 222-228. Association for Computational Linguistics, (2019)Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey.. J. Artif. Intell. Res., (2022)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)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)Inference-only sub-character decomposition improves translation of unseen logographic characters., , and . WAT@AAC/IJCNLPL, page 170-177. Association for Computational Linguistics, (2020)