Enhancing Cross-Lingual Emotion Detection with Data Augmentation and Token-Label Mapping
Jinghui Zhang, Yuan Zhao, Siqin Zhang, Ruijing Zhao, Siyu Bao
Abstract
Cross-lingual emotion detection faces challenges such as imbalanced label distribution, data scarcity, cultural and linguistic differences, figurative language, and the opaqueness of pre-trained language models. This paper presents our approach to the EXALT shared task at WASSA 2024, focusing on emotion transferability across languages and trigger word identification. We employ data augmentation techniques, including back-translation and synonym replacement, to address data scarcity and imbalance issues in the emotion detection sub-task. For the emotion trigger identification sub-task, we utilize token and label mapping to capture emotional information at the subword level. Our system achieves competitive performance, ranking 13th, 1st, and 2nd in the Emotion Detection, Binary Trigger Word Detection, and Numerical Trigger Word Detection tasks.- Anthology ID:
- 2024.wassa-1.53
- Volume:
- Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Orphée De Clercq, Valentin Barriere, Jeremy Barnes, Roman Klinger, João Sedoc, Shabnam Tafreshi
- Venues:
- WASSA | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 528–533
- Language:
- URL:
- https://aclanthology.org/2024.wassa-1.53
- DOI:
- 10.18653/v1/2024.wassa-1.53
- Cite (ACL):
- Jinghui Zhang, Yuan Zhao, Siqin Zhang, Ruijing Zhao, and Siyu Bao. 2024. Enhancing Cross-Lingual Emotion Detection with Data Augmentation and Token-Label Mapping. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 528–533, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Cross-Lingual Emotion Detection with Data Augmentation and Token-Label Mapping (Zhang et al., WASSA-WS 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.wassa-1.53.pdf