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arXiv · 2105.03953

Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation

Zihan Liu·Genta Indra Winata·Pascale Fung
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Abstract

The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems. Fine-tuning a multilingual pre-trained model (e.g., mBART (Liu et al., 2020)) on the translation task is a good approach for low-resource languages; however, its performance will be greatly limited when there are unseen languages in the translation pairs. In this paper, we present a continual pre-training (CPT) framework on mBART to effectively adapt it to unseen languages. We first construct noisy mixed-language text from the monolingual corpus of the target language in the translation pair to cover both the source and target languages, and then, we continue pre-training mBART to reconstruct the original monolingual text. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline, as well as other strong baselines, across all tested low-resource translation pairs containing unseen languages. Furthermore, our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training. The code is available at https://github.com/zliucr/cpt-nmt.

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Zihan Liu, Genta Indra Winata, Pascale Fung. 2021-05-09. Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation. https://arxiv.org/abs/2105.03953

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