The Volctrans Neural Speech Translation System for IWSLT 2021
Abstract
This paper describes the systems submitted to IWSLT 2021 by the Volctrans team. We participate in the offline speech translation and text-to-text simultaneous translation tracks. For offline speech translation, our best end-to-end model achieves 8.1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution. For text-to-text simultaneous translation, we explore the best practice to optimize the wait-k model. As a result, our final submitted systems exceed the benchmark at around 7 BLEU on the same latency regime. We will publish our code and model to facilitate both future research works and industrial applications. This paper describes the systems submitted to IWSLT 2021 by the Volctrans team. We participate in the offline speech translation and text-to-text simultaneous translation tracks. For offline speech translation, our best end-to-end model achieves 7.9 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution. For text-to-text simultaneous translation, we explore the best practice to optimize the wait-k model. As a result, our final submitted systems exceed the benchmark at around 7 BLEU on the same latency regime. We release our code and model at \url{https://github.com/bytedance/neurst/tree/master/examples/iwslt21} to facilitate both future research works and industrial applications.
Keywords
Cite
@article{arxiv.2105.07319,
title = {The Volctrans Neural Speech Translation System for IWSLT 2021},
author = {Chengqi Zhao and Zhicheng Liu and Jian Tong and Tao Wang and Mingxuan Wang and Rong Ye and Qianqian Dong and Jun Cao and Lei Li},
journal= {arXiv preprint arXiv:2105.07319},
year = {2021}
}
Comments
IWSLT 2021