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:
10.18653/v1/2024.semeval-1.261
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2024.semeval-1.261.pdf
Supplementary material:
 2024.semeval-1.261.SupplementaryMaterial.txt