MSCTD: A Multimodal Sentiment Chat Translation Dataset

Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou


Abstract
Multimodal machine translation and textual chat translation have received considerable attention in recent years. Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations. In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context. To this end, we firstly construct a Multimodal Sentiment Chat Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs in 14,762 bilingual dialogues. Each utterance pair, corresponding to the visual context that reflects the current conversational scene, is annotated with a sentiment label. Then, we benchmark the task by establishing multiple baseline systems that incorporate multimodal and sentiment features for MCT. Preliminary experiments on two language directions (English-Chinese) verify the potential of contextual and multimodal information fusion and the positive impact of sentiment on the MCT task. Additionally, we provide a new benchmark on multimodal dialogue sentiment analysis with the constructed MSCTD. Our work can facilitate researches on both multimodal chat translation and multimodal dialogue sentiment analysis.
Anthology ID:
2022.acl-long.186
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2601–2613
Language:
URL:
https://aclanthology.org/2022.acl-long.186
DOI:
10.18653/v1/2022.acl-long.186
Bibkey:
Cite (ACL):
Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, and Jie Zhou. 2022. MSCTD: A Multimodal Sentiment Chat Translation Dataset. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2601–2613, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MSCTD: A Multimodal Sentiment Chat Translation Dataset (Liang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.186.pdf
Code
 xl2248/msctd
Data
BMELDMELDOpenViDial