ACL 2024findings2 citations
MELD-ST: An Emotion-aware Speech Translation Dataset
Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi
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.
BibTeX
@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"
}