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arXiv · 2305.15718

Towards Higher Pareto Frontier in Multilingual Machine Translation

Yichong Huang·Xiaocheng Feng·Xinwei Geng·Baohang Li·Bing Qin
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Abstract

Multilingual neural machine translation has witnessed remarkable progress in recent years. However, the long-tailed distribution of multilingual corpora poses a challenge of Pareto optimization, i.e., optimizing for some languages may come at the cost of degrading the performance of others. Existing balancing training strategies are equivalent to a series of Pareto optimal solutions, which trade off on a Pareto frontier. In this work, we propose a new training framework, Pareto Mutual Distillation (Pareto-MD), towards pushing the Pareto frontier outwards rather than making trade-offs. Specifically, Pareto-MD collaboratively trains two Pareto optimal solutions that favor different languages and allows them to learn from the strengths of each other via knowledge distillation. Furthermore, we introduce a novel strategy to enable stronger communication between Pareto optimal solutions and broaden the applicability of our approach. Experimental results on the widely-used WMT and TED datasets show that our method significantly pushes the Pareto frontier and outperforms baselines by up to +2.46 BLEU.

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Yichong Huang, Xiaocheng Feng, Xinwei Geng, Baohang Li, Bing Qin. 2023-05-25. Towards Higher Pareto Frontier in Multilingual Machine Translation. https://arxiv.org/abs/2305.15718

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