@inproceedings{zhao-etal-2021-neurst,
title = "{N}eur{ST}: Neural Speech Translation Toolkit",
author = "Zhao, Chengqi and
Wang, Mingxuan and
Dong, Qianqian and
Ye, Rong and
Li, Lei",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.7",
doi = "10.18653/v1/2021.acl-demo.7",
pages = "55--62",
abstract = "NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced speech translation research and products. NeurST aims at facilitating the speech translation research for NLP researchers and building reliable benchmarks for this field. It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. In this paper, we will introduce the framework design of NeurST and show experimental results for different benchmark datasets, which can be regarded as reliable baselines for future research. The toolkit is publicly available at \url{https://github.com/bytedance/neurst} and we will continuously update the performance of with other counterparts and studies at \url{https://st-benchmark.github.io/}.",
}
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<abstract>NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced speech translation research and products. NeurST aims at facilitating the speech translation research for NLP researchers and building reliable benchmarks for this field. It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. In this paper, we will introduce the framework design of NeurST and show experimental results for different benchmark datasets, which can be regarded as reliable baselines for future research. The toolkit is publicly available at https://github.com/bytedance/neurst and we will continuously update the performance of with other counterparts and studies at https://st-benchmark.github.io/.</abstract>
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%0 Conference Proceedings
%T NeurST: Neural Speech Translation Toolkit
%A Zhao, Chengqi
%A Wang, Mingxuan
%A Dong, Qianqian
%A Ye, Rong
%A Li, Lei
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhao-etal-2021-neurst
%X NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced speech translation research and products. NeurST aims at facilitating the speech translation research for NLP researchers and building reliable benchmarks for this field. It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. In this paper, we will introduce the framework design of NeurST and show experimental results for different benchmark datasets, which can be regarded as reliable baselines for future research. The toolkit is publicly available at https://github.com/bytedance/neurst and we will continuously update the performance of with other counterparts and studies at https://st-benchmark.github.io/.
%R 10.18653/v1/2021.acl-demo.7
%U https://aclanthology.org/2021.acl-demo.7
%U https://doi.org/10.18653/v1/2021.acl-demo.7
%P 55-62
Markdown (Informal)
[NeurST: Neural Speech Translation Toolkit](https://aclanthology.org/2021.acl-demo.7) (Zhao et al., ACL-IJCNLP 2021)
ACL
- Chengqi Zhao, Mingxuan Wang, Qianqian Dong, Rong Ye, and Lei Li. 2021. NeurST: Neural Speech Translation Toolkit. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 55–62, Online. Association for Computational Linguistics.