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arXiv · 2103.06964

Learning Policies for Multilingual Training of Neural Machine Translation Systems

Gaurav Kumar·Philipp Koehn·Sanjeev Khudanpur
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Abstract

Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper, we propose two simple search based curricula -- orderings of the multilingual training data -- which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally, we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system with the aid of contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.

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Gaurav Kumar, Philipp Koehn, Sanjeev Khudanpur. 2021-03-11. Learning Policies for Multilingual Training of Neural Machine Translation Systems. https://arxiv.org/abs/2103.06964

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