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An Empirical Exploration of Curriculum Learning for Neural Machine Translation., , , , , , , , and . CoRR, (2018)The JHU Parallel Corpus Filtering Systems for WMT 2018., , and . WMT (shared task), page 896-899. Association for Computational Linguistics, (2018)The JHU Machine Translation Systems for WMT 2017., , , , , and . WMT, page 276-282. Association for Computational Linguistics, (2017)Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation., , , and . AMTA, page 157-174. Association for Machine Translation in the Americas, (2022)Perplexity-Driven Case Encoding Needs Augmentation for CAPITALIZATION Robustness., , , and . IJCNLP (2), page 146-156. Association for Computational Linguistics, (2023)Simulated multiple reference training improves low-resource machine translation., , , and . EMNLP (1), page 82-89. Association for Computational Linguistics, (2020)SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training., and . CoRR, (2020)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)Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation., , , and . NMT@ACL, page 36-44. Association for Computational Linguistics, (2018)Revisiting Locality Sensitive Hashing for Vocabulary Selection in Fast Neural Machine Translation., , , and . WMT, page 855-869. Association for Computational Linguistics, (2022)