Towards Making the Most of BERT in Neural Machine Translation

GPT-2 and BERT demonstrate the effectiveness of using pretrained language models (LMs) on various natural language processing tasks. However, LM fine-tuning often suffers from catastrophic forgetting when applied to resource-rich tasks. In this work, we introduce a concerted training framework (CTNMT) that is the key to integrate the pre-trained LMs to neural machine translation (NMT). Our proposed CTNMT consists of three techniques: a) asymptotic distillation to ensure that the NMT model can retain the previous pre-trained knowledge; b) a dynamic switching gate to avoid catastrophic forgetting of pre-trained knowledge; and c) a strategy to adjust the learning paces according to a scheduled policy. Our experiments in machine translation show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pretraining aided NMT by 1. 4 BLEU score. While for the large WMT14 English-French task with 40 millions of sentencepairs, our base model still significantly improves upon the state-of-the-art Transformer big model by more than 1 BLEU score.

Authors

• Jiacheng Yang
• Mingxuan Wang Bytedance Seed, China
• Hao Zhou Naval University of Engineering
• Chengqi Zhao ByteDance AI Lab
• Weinan Zhang Shanghai Jiao Tong University
• Yong Yu Shanghai Jiao Tong University
• Lei Li Cleveland State University

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