switch-GLAT: Multilingual Parallel Machine Translation Via Code-Switch Decoder
Zhenqiao Song, Hao Zhou, Lihua Qian, Jingjing Xu, Shanbo Cheng, Mingxuan Wang, Lei Li
Abstract
Multilingual machine translation aims to develop a single model for multiple language directions. However, existing multilingual models based on Transformer are limited in terms of both translation performance and inference speed. In this paper, we propose switch-GLAT, a non-autoregressive multilingual machine translation model with a code-switch decoder. It can generate contextual code-switched translations for a given source sentence, and perform code-switch back-translation, greatly boosting multilingual translation performance. In addition, its inference is highly efficient thanks to its parallel decoder. Experiments show that our proposed switch-GLAT outperform the multilingual Transformer with as much as 0.74 BLEU improvement and 6.2x faster decoding speed in inference.
BibTeX
@inproceedings{
song2022switchglat,
title={switch-{GLAT}: Multilingual Parallel Machine Translation Via Code-Switch Decoder},
author={Zhenqiao Song and Hao Zhou and Lihua Qian and Jingjing Xu and Shanbo Cheng and Mingxuan Wang and Lei Li},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=5HvpvYd68b}
}