Explicit Sentence Compression for Neural Machine Translation

Authors

  • Zuchao Li Shanghai Jiao Tong University
  • Rui Wang National Institute of Information and Communications Technology
  • Kehai Chen National Institute of Information and Communications Technology
  • Masao Utiyama National Institute of Information and Communications Technology
  • Eiichiro Sumita National Institute of Information and Communications Technology
  • Zhuosheng Zhang Shanghai Jiao Tong University
  • Hai Zhao Shanghai Jiao Tong University

DOI:

https://doi.org/10.1609/aaai.v34i05.6347

Abstract

State-of-the-art Transformer-based neural machine translation (NMT) systems still follow a standard encoder-decoder framework, in which source sentence representation can be well done by an encoder with self-attention mechanism. Though Transformer-based encoder may effectively capture general information in its resulting source sentence representation, the backbone information, which stands for the gist of a sentence, is not specifically focused on. In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT. In practice, an explicit sentence compression goal used to learn the backbone information in a sentence. We propose three ways, including backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the compressed sentence into NMT. Our empirical tests on the WMT English-to-French and English-to-German translation tasks show that the proposed sentence compression method significantly improves the translation performances over strong baselines.

Downloads

Published

2020-04-03

How to Cite

Li, Z., Wang, R., Chen, K., Utiyama, M., Sumita, E., Zhang, Z., & Zhao, H. (2020). Explicit Sentence Compression for Neural Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8311-8318. https://doi.org/10.1609/aaai.v34i05.6347

Issue

Section

AAAI Technical Track: Natural Language Processing