Abstract
This paper presents a detailed system description of our entry which finished 1st with a large lead at WASSA 2024 Task 2, focused on cross-lingual emotion detection. We utilized a combination of large language models (LLMs) and their ensembles to effectively understand and categorize emotions across different languages. Our approach not only outperformed other submissions with a large margin, but also demonstrated the strength of integrating multiple models to enhance performance. Additionally, We conducted a thorough comparison of the benefits and limitations of each model used. An error analysis is included along with suggested areas for future improvement. This paper aims to offer a clear and comprehensive understanding of advanced techniques in emotion detection, making it accessible even to those new to the field.- Anthology ID:
- 2024.wassa-1.44
- 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:
- 464–469
- Language:
- URL:
- https://aclanthology.org/2024.wassa-1.44
- DOI:
- Cite (ACL):
- Ram Mohan Rao Kadiyala. 2024. Cross-lingual Emotion Detection through Large Language Models. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 464–469, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Emotion Detection through Large Language Models (Kadiyala, WASSA-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.wassa-1.44.pdf