Existing multilingual machine translation approaches mainly focus on
English-centric directions, while the non-English directions still lag behind.
In this work, we aim to build a many-to-many translation system with an
emphasis on the quality of non-English language directions. Our intuition is
based on the hypothesis that a universal cross-language representation leads to
better multilingual translation performance. To this end, we propose mRASP2, a
training method to obtain a single unified multilingual translation model.
mRASP2 is empowered by two techniques: a) a contrastive learning scheme to
close the gap among representations of different languages, and b) data
augmentation on both multiple parallel and monolingual data to further align
token representations. For English-centric directions, mRASP2 outperforms
existing best unified model and achieves competitive or even better performance
than the pre-trained and fine-tuned model mBART on tens of WMT's translation
directions. For non-English directions, mRASP2 achieves an improvement of
average 10+ BLEU compared with the multilingual Transformer baseline. Code,
data and trained models are available at this https URL

本论文旨在建立一个多对多的机器翻译系统，重点关注非英语语言方向的质量。为此，我们提出了一种训练方法 mRASP2，通过对不同语言的表示进行对比学习和数据增强来获得单一统一的跨语言翻译模型，该模型在英语为中心的方向中表现优异，在非英语方向中比多语言 Transformer 基准模型提高 10+ BLEU 分数。