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Google's neural machine translation system: Bridging the gap between human and machine translation

, , , , , , , , , , and . arXiv preprint arXiv:1609.08144, (2016)

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Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, , , , , , , , , and 21 other author(s). (2016)cite arxiv:1609.08144.Using Word Probabilities as Confidence Measures, , and . Int.~Conf.~on Acoustics, Speech, and Signal Processing (ICASSP), 1, page 225--228. Seattle, Washington, USA, (May 1998)Comparison of Alignment Templates and Maximum Entropy Models for Natural Language Understanding, , , and . EACL, page 11--18. Budapest, Hungary, (April 2003)Creating a High-Quality Machine Translation System for a Low-Resource Language: Yiddish., , and . MTSummit, (2009)Confidence measures for statistical machine translation., , and . MTSummit, (2003)Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, , , , , , , , , and 20 other author(s). arXiv.org, (September 2016)Improved Domain Adaptation for Statistical Machine Translation., , , , and . AMTA, Association for Machine Translation in the Americas, (2020)Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling., , , , , , , , , and 81 other author(s). CoRR, (2019)Applications of Statistical Machine Translation Approaches to Spoken Language Understanding., , and . IEEE Trans. Speech Audio Process., 17 (4): 803-818 (2009)Scoring Criteria for Tree based Dialogue Course Management, and . ISCA Tutorial and Research Workshop Multi-Modal Dialogue in Mobile Environments, Kloster Irsee, Germany, (June 2002)