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Neural Lattice Search for Domain Adaptation in Machine Translation.

, , , , and . IJCNLP(2), page 20-25. Asian Federation of Natural Language Processing, (2017)

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The JHU Machine Translation Systems for WMT 2016., , , , and . WMT, page 272-280. The Association for Computer Linguistics, (2016)Paradigm Completion for Derivational Morphology., , , , and . EMNLP, page 714-720. Association for Computational Linguistics, (2017)Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting., , , , , , and . NAACL-HLT (1), page 839-850. Association for Computational Linguistics, (2019)Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation., , , , and . NAACL-HLT (1), page 2062-2068. Association for Computational Linguistics, (2019)An Interactive Teaching Tool for Introducing Novices to Machine Translation., , , and . SIGCSE, page 1276. ACM, (2019)Deep Generalized Canonical Correlation Analysis., , , , , and . RepL4NLP@ACL, page 1-6. Association for Computational Linguistics, (2019)SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training., and . CoRR, (2020)On-the-Fly Fusion of Large Language Models and Machine Translation., , and . CoRR, (2023)Revisiting Locality Sensitive Hashing for Vocabulary Selection in Fast Neural Machine Translation., , , and . WMT, page 855-869. Association for Computational Linguistics, (2022)The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education., , , , , and . NGT@ACL, page 188-197. Association for Computational Linguistics, (2020)