Balancing Training for Multilingual Neural Machine Translation

• URL: http://arxiv.org/abs/2004.06748v4
• Date: Sat, 5 Sep 2020 22:55:01 GMT
• Title: Balancing Training for Multilingual Neural Machine Translation
• Authors: Xinyi Wang, Yulia Tsvetkov, Graham Neubig
• Abstract summary: multilingual machine translation (MT) models can translate to/from multiple languages. Standard practice is to up-sample less resourced languages to increase representation. We propose a method that instead automatically learns how to weight training data through a data scorer.
• Score: 130.54253367251738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When training multilingual machine translation (MT) models that can translate to/from multiple languages, we are faced with imbalanced training sets: some languages have much more training data than others. Standard practice is to up-sample less resourced languages to increase representation, and the degree of up-sampling has a large effect on the overall performance. In this paper, we propose a method that instead automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages. Experiments on two sets of languages under both one-to-many and many-to-one MT settings show our method not only consistently outperforms heuristic baselines in terms of average performance, but also offers flexible control over the performance of which languages are optimized.

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