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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.SQuId: Measuring Speech Naturalness in Many Languages., , , , , and . CoRR, (2022)Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, , , , , , , , , and 20 other author(s). arXiv.org, (September 2016)SQuId: Measuring Speech Naturalness in Many Languages., , , , , and . ICASSP, page 1-5. IEEE, (2023)Feature-Rich Language-Independent Syntax-Based Alignment for Statistical Machine Translation., , and . EMNLP, page 497-507. ACL, (2011)Small and Practical BERT Models for Sequence Labeling., , , , , and . EMNLP/IJCNLP (1), page 3630-3634. Association for Computational Linguistics, (2019)Multimodal Modeling For Spoken Language Identification., , , , , , , , , and 3 other author(s). CoRR, (2023)Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition., , , , and . CoRR, (2020)Improving Multilingual Models with Language-Clustered Vocabularies., , , and . EMNLP (1), page 4536-4546. Association for Computational Linguistics, (2020)XTREME-S: Evaluating Cross-lingual Speech Representations., , , , , , , , , and 9 other author(s). INTERSPEECH, page 3248-3252. ISCA, (2022)