The VolcTrans System for WMT22 Multilingual Machine Translation Task

• URL: http://arxiv.org/abs/2210.11599v1
• Date: Thu, 20 Oct 2022 21:18:03 GMT
• Title: The VolcTrans System for WMT22 Multilingual Machine Translation Task
• Authors: Xian Qian, Kai Hu, Jiaqiang Wang, Yifeng Liu, Xingyuan Pan, Jun Cao, Mingxuan Wang
• Abstract summary: VolcTrans is a transformerbased multilingual model trained on data from multiple sources. A series of rules clean both bilingual and monolingual texts. Our system achieves 17.3 BLEU, 21.9 spBLEU, and 41.9 chrF2++ on average over all language pairs.
• Score: 24.300726424411007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This report describes our VolcTrans system for the WMT22 shared task on large-scale multilingual machine translation. We participated in the unconstrained track which allows the use of external resources. Our system is a transformerbased multilingual model trained on data from multiple sources including the public training set from the data track, NLLB data provided by Meta AI, self-collected parallel corpora, and pseudo bitext from back-translation. A series of heuristic rules clean both bilingual and monolingual texts. On the official test set, our system achieves 17.3 BLEU, 21.9 spBLEU, and 41.9 chrF2++ on average over all language pairs. The average inference speed is 11.5 sentences per second using a single Nvidia Tesla V100 GPU. Our code and trained models are available at https://github.com/xian8/wmt22

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