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arXiv 2204.04306cs.CLcs.AIcs.CY

MMTAfrica: Multilingual Machine Translation for African Languages

Technical University of Munich(慕尼黑工业大学);Mila Quebec AI Institute(米拉魁北克人工智能研究所);Masakhane;Jacobs University Bremen(不来梅雅各布大学)

Chris C. Emezue, Bonaventure F. P. Dossou

英文摘要:

In this paper, we focus on the task of multilingual machine translation for African languages and describe our contribution in the 2021 WMT Shared Task: Large-Scale Multilingual Machine Translation. We introduce MMTAfrica, the first many-to-many multilingual translation system for six African languages: Fon (fon), Igbo (ibo), Kinyarwanda (kin), Swahili/Kiswahili (swa), Xhosa (xho), and Yoruba (yor) and two non-African languages: English (eng) and French (fra). For multilingual translation concerning African languages, we introduce a novel backtranslation and reconstruction objective, BT\&REC, inspired by the random online back translation and T5 modeling framework respectively, to effectively leverage monolingual data. Additionally, we report improvements from MMTAfrica over the FLORES 101 benchmarks (spBLEU gains ranging from $+0.58$ in Swahili to French to $+19.46$ in French to Xhosa). We release our dataset and code source at https://github.com/edaiofficial/mmtafrica.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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