ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification
Meredith Gibbons, Maggie Mi, Xingyi Song, Aline Villavicencio
Abstract
This paper presents our findings for SemEval2024 Task 4. We submit only to subtask 1, applying the text-to-text framework using a FLAN-T5 model with a combination of parameter efficient fine-tuning methods - low-rankadaptation and prompt tuning. Overall, we find that the system performs well in English, but performance is limited in Bulgarian, North Macedonian and Arabic. Our analysis raises interesting questions about the effects of labelorder and label names when applying the text-to-text framework.- Anthology ID:
- 2024.semeval-1.261
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1860–1867
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.261
- DOI:
- Cite (ACL):
- Meredith Gibbons, Maggie Mi, Xingyi Song, and Aline Villavicencio. 2024. ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1860–1867, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification (Gibbons et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.261.pdf