@inproceedings{zhao-etal-2021-volctrans,
title = "The Volctrans Neural Speech Translation System for {IWSLT} 2021",
author = "Zhao, Chengqi and
Liu, Zhicheng and
Tong, Jian and
Wang, Tao and
Wang, Mingxuan and
Ye, Rong and
Dong, Qianqian and
Cao, Jun and
Li, Lei",
editor = "Federico, Marcello and
Waibel, Alex and
Costa-juss{\`a}, Marta R. and
Niehues, Jan and
Stuker, Sebastian and
Salesky, Elizabeth",
booktitle = "Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)",
month = aug,
year = "2021",
address = "Bangkok, Thailand (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.iwslt-1.6",
doi = "10.18653/v1/2021.iwslt-1.6",
pages = "64--74",
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 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 to facilitate both future research works and industrial applications.",
}
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<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 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 to facilitate both future research works and industrial applications.</abstract>
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%0 Conference Proceedings
%T The Volctrans Neural Speech Translation System for IWSLT 2021
%A Zhao, Chengqi
%A Liu, Zhicheng
%A Tong, Jian
%A Wang, Tao
%A Wang, Mingxuan
%A Ye, Rong
%A Dong, Qianqian
%A Cao, Jun
%A Li, Lei
%Y Federico, Marcello
%Y Waibel, Alex
%Y Costa-jussà, Marta R.
%Y Niehues, Jan
%Y Stuker, Sebastian
%Y Salesky, Elizabeth
%S Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand (online)
%F zhao-etal-2021-volctrans
%X 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 to facilitate both future research works and industrial applications.
%R 10.18653/v1/2021.iwslt-1.6
%U https://aclanthology.org/2021.iwslt-1.6
%U https://doi.org/10.18653/v1/2021.iwslt-1.6
%P 64-74
Markdown (Informal)
[The Volctrans Neural Speech Translation System for IWSLT 2021](https://aclanthology.org/2021.iwslt-1.6) (Zhao et al., IWSLT 2021)
ACL
- Chengqi Zhao, Zhicheng Liu, Jian Tong, Tao Wang, Mingxuan Wang, Rong Ye, Qianqian Dong, Jun Cao, and Lei Li. 2021. The Volctrans Neural Speech Translation System for IWSLT 2021. In Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), pages 64–74, Bangkok, Thailand (online). Association for Computational Linguistics.