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The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation., , , , , , , , , and 6 other author(s). ACL (1), page 76-86. Association for Computational Linguistics, (2018)Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning., , , , , , and . CoRR, (2022)Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges., , , , , , , , , and 3 other author(s). CoRR, (2019)Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation., , , , , , , and . ACL, page 2827-2835. Association for Computational Linguistics, (2020)Faster Transformer Decoding: N-gram Masked Self-Attention., , , and . CoRR, (2020)Building Machine Translation Systems for the Next Thousand Languages., , , , , , , , , and 14 other author(s). CoRR, (2022)Rapid Domain Adaptation for Machine Translation with Monolingual Data., , , and . CoRR, (2020)Towards End-to-End In-Image Neural Machine Translation., , , , , and . CoRR, (2020)Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling., , , , , , , , , and 81 other author(s). CoRR, (2019)GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism., , , , , , , , , and 1 other author(s). NeurIPS, page 103-112. (2019)