On the Pareto Front of Multilingual Neural Machine Translation

• URL: http://arxiv.org/abs/2304.03216v3
• Date: Tue, 31 Oct 2023 15:58:53 GMT
• Title: On the Pareto Front of Multilingual Neural Machine Translation
• Authors: Liang Chen and Shuming Ma and Dongdong Zhang and Furu Wei and Baobao Chang
• Abstract summary: We study how the performance of a given direction changes with its sampling ratio in Neural Machine Translation (MNMT) We propose the Double Power Law to predict the unique performance trade-off front in MNMT. In our experiments, it achieves better performance than temperature searching and gradient manipulation methods with only 1/5 to 1/2 of the total training budget.
• Score: 123.94355117635293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we study how the performance of a given direction changes with its sampling ratio in Multilingual Neural Machine Translation (MNMT). By training over 200 multilingual models with various model sizes, data sizes, and language directions, we find it interesting that the performance of certain translation direction does not always improve with the increase of its weight in the multi-task optimization objective. Accordingly, scalarization method leads to a multitask trade-off front that deviates from the traditional Pareto front when there exists data imbalance in the training corpus, which poses a great challenge to improve the overall performance of all directions. Based on our observations, we propose the Double Power Law to predict the unique performance trade-off front in MNMT, which is robust across various languages, data adequacy, and the number of tasks. Finally, we formulate the sample ratio selection problem in MNMT as an optimization problem based on the Double Power Law. In our experiments, it achieves better performance than temperature searching and gradient manipulation methods with only 1/5 to 1/2 of the total training budget. We release the code at https://github.com/pkunlp-icler/ParetoMNMT for reproduction.

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