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Learning Policies for Multilingual Training of Neural Machine Translation Systems

2021-03-11 21:38:04
Gaurav Kumar, Philipp Koehn, Sanjeev Khudanpur
     

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.

Abstract (translated)

URL

https://arxiv.org/abs/2103.06964

PDF

https://arxiv.org/pdf/2103.06964.pdf


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