Abstract
This paper presents HW-TSC’s submissions to CCMT 2022 Chinese Minority Language Translation task. We participate in three language directions: Mongolian\(\rightarrow \)Chinese Daily Conversation Translation, Tibetan\(\rightarrow \)Chinese Government Document Translation, and Uighur\(\rightarrow \)Chinese News Translation. We train our models using the Deep Transformer architecture, and adopt enhancement strategies such as Regularized Dropout, Tagged Back-Translation, Alternated Training, and Ensemble. Our enhancement experiments have proved the effectiveness of above-mentioned strategies. We submit enhanced systems as primary systems for the three tracks. In addition, we train contrast models using additional bilingual data and submit results generated by these contrast models.
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Wu, Z. et al. (2022). Multi-strategy Enhanced Neural Machine Translation for Chinese Minority Languages. In: Xiao, T., Pino, J. (eds) Machine Translation. CCMT 2022. Communications in Computer and Information Science, vol 1671. Springer, Singapore. https://doi.org/10.1007/978-981-19-7960-6_4
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DOI: https://doi.org/10.1007/978-981-19-7960-6_4
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