Dynamic Attention Aggregation with BERT for Neural Machine Translation

Jia-Rui Zhang, Hongzheng Li, Shumin Shi, Heyan Huang, Yue Hu, Xiangpeng Wei

Abstract: The recently proposed BERT has demonstrated great power in various natural language processing tasks. However, the model does not perform effectively on cross-lingual tasks, especially on machine translation. In this work, we propose three methods to introduce pre-trained BERT into neural machine translation without fine-tuning. Our approach consists of a) a linear-attention aggregation that leverages a parameter matrix to capture the key knowledge of BERT, b) a self-attention aggregation which aims to learn what is vital for input and output, and c) a switch-gate aggregation to dynamically control the balance of the information flowing from the pre-trained BERT or the NMT model. We conduct experiments on several translation benchmarks and substantially improve over 2 BELU points on the IWSLT’14 English - German task with switch-gate aggregation method compared to a strong baseline, while our proposed model also performs remarkably on the other tasks.