Self-supervised and Supervised Joint Training for Resource-rich Machine Translation

• URL: http://arxiv.org/abs/2106.04060v1
• Date: Tue, 8 Jun 2021 02:35:40 GMT
• Title: Self-supervised and Supervised Joint Training for Resource-rich Machine Translation
• Authors: Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey
• Abstract summary: Self-supervised pre-training of text representations has been successfully applied to low-resource Neural Machine Translation (NMT) We propose a joint training approach, $F$-XEnDec, to combine self-supervised and supervised learning to optimize NMT models.
• Score: 30.502625878505732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised pre-training of text representations has been successfully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve notable gains on resource-rich NMT. In this paper, we propose a joint training approach, $F_2$-XEnDec, to combine self-supervised and supervised learning to optimize NMT models. To exploit complementary self-supervised signals for supervised learning, NMT models are trained on examples that are interbred from monolingual and parallel sentences through a new process called crossover encoder-decoder. Experiments on two resource-rich translation benchmarks, WMT'14 English-German and WMT'14 English-French, demonstrate that our approach achieves substantial improvements over several strong baseline methods and obtains a new state of the art of 46.19 BLEU on English-French when incorporating back translation. Results also show that our approach is capable of improving model robustness to input perturbations such as code-switching noise which frequently appears on social media.

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