@inproceedings{chen-etal-2024-meld,
title = "{MELD}-{ST}: An Emotion-aware Speech Translation Dataset",
author = "Chen, Sirou and
Yahata, Sakiko and
Shimizu, Shuichiro and
Yang, Zhengdong and
Li, Yihang and
Chu, Chenhui and
Kurohashi, Sadao",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.601",
doi = "10.18653/v1/2024.findings-acl.601",
pages = "10118--10126",
abstract = "Emotion plays a crucial role in human conversation. This paper underscores the significance of considering emotion in speech translation. We present the MELD-ST dataset for the emotion-aware speech translation task, comprising English-to-Japanese and English-to-German language pairs. Each language pair includes about 10,000 utterances annotated with emotion labels from the MELD dataset. Baseline experiments using the SeamlessM4T model on the dataset indicate that fine-tuning with emotion labels can enhance translation performance in some settings, highlighting the need for further research in emotion-aware speech translation systems.",
}
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<abstract>Emotion plays a crucial role in human conversation. This paper underscores the significance of considering emotion in speech translation. We present the MELD-ST dataset for the emotion-aware speech translation task, comprising English-to-Japanese and English-to-German language pairs. Each language pair includes about 10,000 utterances annotated with emotion labels from the MELD dataset. Baseline experiments using the SeamlessM4T model on the dataset indicate that fine-tuning with emotion labels can enhance translation performance in some settings, highlighting the need for further research in emotion-aware speech translation systems.</abstract>
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%0 Conference Proceedings
%T MELD-ST: An Emotion-aware Speech Translation Dataset
%A Chen, Sirou
%A Yahata, Sakiko
%A Shimizu, Shuichiro
%A Yang, Zhengdong
%A Li, Yihang
%A Chu, Chenhui
%A Kurohashi, Sadao
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F chen-etal-2024-meld
%X Emotion plays a crucial role in human conversation. This paper underscores the significance of considering emotion in speech translation. We present the MELD-ST dataset for the emotion-aware speech translation task, comprising English-to-Japanese and English-to-German language pairs. Each language pair includes about 10,000 utterances annotated with emotion labels from the MELD dataset. Baseline experiments using the SeamlessM4T model on the dataset indicate that fine-tuning with emotion labels can enhance translation performance in some settings, highlighting the need for further research in emotion-aware speech translation systems.
%R 10.18653/v1/2024.findings-acl.601
%U https://aclanthology.org/2024.findings-acl.601
%U https://doi.org/10.18653/v1/2024.findings-acl.601
%P 10118-10126
Markdown (Informal)
[MELD-ST: An Emotion-aware Speech Translation Dataset](https://aclanthology.org/2024.findings-acl.601) (Chen et al., Findings 2024)
ACL
- Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, and Sadao Kurohashi. 2024. MELD-ST: An Emotion-aware Speech Translation Dataset. In Findings of the Association for Computational Linguistics: ACL 2024, pages 10118–10126, Bangkok, Thailand. Association for Computational Linguistics.