@inproceedings{guo-etal-2023-hw,
title = "The {HW}-{TSC}{'}s Simultaneous Speech-to-Text Translation System for {IWSLT} 2023 Evaluation",
author = "Guo, Jiaxin and
Wei, Daimeng and
Wu, Zhanglin and
Li, Zongyao and
Rao, Zhiqiang and
Wang, Minghan and
Shang, Hengchao and
Chen, Xiaoyu and
Yu, Zhengzhe and
Li, Shaojun and
Xie, Yuhao and
Lei, Lizhi and
Yang, Hao",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwslt-1.35",
doi = "10.18653/v1/2023.iwslt-1.35",
pages = "376--382",
abstract = "In this paper, we present our submission to the IWSLT 2023 Simultaneous Speech-to-Text Translation competition. Our participation involves three language directions: English-German, English-Chinese, and English-Japanese. Our proposed solution is a cascaded incremental decoding system that comprises an ASR model and an MT model. The ASR model is based on the U2++ architecture and can handle both streaming and offline speech scenarios with ease. Meanwhile, the MT model adopts the Deep-Transformer architecture. To improve performance, we explore methods to generate a confident partial target text output that guides the next MT incremental decoding process. In our experiments, we demonstrate that our simultaneous strategies achieve low latency while maintaining a loss of no more than 2 BLEU points when compared to offline systems.",
}
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%0 Conference Proceedings
%T The HW-TSC’s Simultaneous Speech-to-Text Translation System for IWSLT 2023 Evaluation
%A Guo, Jiaxin
%A Wei, Daimeng
%A Wu, Zhanglin
%A Li, Zongyao
%A Rao, Zhiqiang
%A Wang, Minghan
%A Shang, Hengchao
%A Chen, Xiaoyu
%A Yu, Zhengzhe
%A Li, Shaojun
%A Xie, Yuhao
%A Lei, Lizhi
%A Yang, Hao
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada (in-person and online)
%F guo-etal-2023-hw
%X In this paper, we present our submission to the IWSLT 2023 Simultaneous Speech-to-Text Translation competition. Our participation involves three language directions: English-German, English-Chinese, and English-Japanese. Our proposed solution is a cascaded incremental decoding system that comprises an ASR model and an MT model. The ASR model is based on the U2++ architecture and can handle both streaming and offline speech scenarios with ease. Meanwhile, the MT model adopts the Deep-Transformer architecture. To improve performance, we explore methods to generate a confident partial target text output that guides the next MT incremental decoding process. In our experiments, we demonstrate that our simultaneous strategies achieve low latency while maintaining a loss of no more than 2 BLEU points when compared to offline systems.
%R 10.18653/v1/2023.iwslt-1.35
%U https://aclanthology.org/2023.iwslt-1.35
%U https://doi.org/10.18653/v1/2023.iwslt-1.35
%P 376-382
Markdown (Informal)
[The HW-TSC’s Simultaneous Speech-to-Text Translation System for IWSLT 2023 Evaluation](https://aclanthology.org/2023.iwslt-1.35) (Guo et al., IWSLT 2023)
ACL
- Jiaxin Guo, Daimeng Wei, Zhanglin Wu, Zongyao Li, Zhiqiang Rao, Minghan Wang, Hengchao Shang, Xiaoyu Chen, Zhengzhe Yu, Shaojun Li, Yuhao Xie, Lizhi Lei, and Hao Yang. 2023. The HW-TSC’s Simultaneous Speech-to-Text Translation System for IWSLT 2023 Evaluation. In Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), pages 376–382, Toronto, Canada (in-person and online). Association for Computational Linguistics.