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Extracting In-domain Training Corpora for Neural Machine Translation Using Data Selection Methods., , , and . WMT, page 224-231. Association for Computational Linguistics, (2018)Combining PBSMT and NMT Back-translated Data for Efficient NMT., , , , and . RANLP, page 922-931. INCOMA Ltd., (2019)ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task., , , and . IJCNLP (Shared Tasks), page 67-72. Asian Federation of Natural Language Processing, (2017)Rakuten's Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity., , , , , and . WAT@COLING, page 68-72. International Conference on Computational Linguistics, (2022)Extending Feature Decay Algorithms Using Alignment Entropy., , and . FETLT, volume 10341 of Lecture Notes in Computer Science, page 170-182. Springer, (2016)Investigating Backtranslation in Neural Machine Translation., , , , and . EAMT, page 269-278. (2018)Feature Decay Algorithms for Neural Machine Translation., , and . EAMT, page 259-268. (2018)Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian., and . LoResMT@COLING, page 84-92. Association for Computational Linguistics, (2022)Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation., , and . PSLT@MTSummit, page 13-23. European Association for Machine Translation, (2019)Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation., , , and . ACL, page 3898-3908. Association for Computational Linguistics, (2020)