@inproceedings{jiawei-etal-2024-hw,
title = "{HW}-{TSC}{'}s Submissions To the {IWSLT}2024 Low-resource Speech Translation Tasks",
author = "Jiawei, Zheng and
Shang, Hengchao and
Li, Zongyao and
Wu, Zhanglin and
Wei, Daimeng and
Rao, Zhiqiang and
Li, Shaojun and
Guo, Jiaxin and
Wei, Bin and
Luo, Yuanchang and
Yang, Hao",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.21",
pages = "160--163",
abstract = "In this work, we submitted our systems to the low-resource track of the IWSLT 2024 Speech Translation Campaign. Our systems tackled the unconstrained condition of the Dialectal Arabic North Levantine (ISO-3 code: apc) to English language pair. We proposed a cascaded solution consisting of an automatic speech recognition (ASR) model and a machine translation (MT) model. It was noted that the ASR model employed the pre-trained Whisper-large-v3 model to process the speech data, while the MT model adopted the Transformer architecture. To improve the quality of the MT model, it was stated that our system utilized not only the data provided by the competition but also an additional 54 million parallel sentences. Ultimately, we reported that our final system achieved a BLEU score of 24.7 for apc-to-English translation.",
}
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<abstract>In this work, we submitted our systems to the low-resource track of the IWSLT 2024 Speech Translation Campaign. Our systems tackled the unconstrained condition of the Dialectal Arabic North Levantine (ISO-3 code: apc) to English language pair. We proposed a cascaded solution consisting of an automatic speech recognition (ASR) model and a machine translation (MT) model. It was noted that the ASR model employed the pre-trained Whisper-large-v3 model to process the speech data, while the MT model adopted the Transformer architecture. To improve the quality of the MT model, it was stated that our system utilized not only the data provided by the competition but also an additional 54 million parallel sentences. Ultimately, we reported that our final system achieved a BLEU score of 24.7 for apc-to-English translation.</abstract>
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%0 Conference Proceedings
%T HW-TSC’s Submissions To the IWSLT2024 Low-resource Speech Translation Tasks
%A Jiawei, Zheng
%A Shang, Hengchao
%A Li, Zongyao
%A Wu, Zhanglin
%A Wei, Daimeng
%A Rao, Zhiqiang
%A Li, Shaojun
%A Guo, Jiaxin
%A Wei, Bin
%A Luo, Yuanchang
%A Yang, Hao
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand (in-person and online)
%F jiawei-etal-2024-hw
%X In this work, we submitted our systems to the low-resource track of the IWSLT 2024 Speech Translation Campaign. Our systems tackled the unconstrained condition of the Dialectal Arabic North Levantine (ISO-3 code: apc) to English language pair. We proposed a cascaded solution consisting of an automatic speech recognition (ASR) model and a machine translation (MT) model. It was noted that the ASR model employed the pre-trained Whisper-large-v3 model to process the speech data, while the MT model adopted the Transformer architecture. To improve the quality of the MT model, it was stated that our system utilized not only the data provided by the competition but also an additional 54 million parallel sentences. Ultimately, we reported that our final system achieved a BLEU score of 24.7 for apc-to-English translation.
%U https://aclanthology.org/2024.iwslt-1.21
%P 160-163
Markdown (Informal)
[HW-TSC’s Submissions To the IWSLT2024 Low-resource Speech Translation Tasks](https://aclanthology.org/2024.iwslt-1.21) (Jiawei et al., IWSLT 2024)
ACL
- Zheng Jiawei, Hengchao Shang, Zongyao Li, Zhanglin Wu, Daimeng Wei, Zhiqiang Rao, Shaojun Li, Jiaxin Guo, Bin Wei, Yuanchang Luo, and Hao Yang. 2024. HW-TSC’s Submissions To the IWSLT2024 Low-resource Speech Translation Tasks. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 160–163, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.