@inproceedings{gibbons-etal-2024-shefcdteam,
title = "{S}hef{CDT}eam at {S}em{E}val-2024 Task 4: A Text-to-Text Model for Multi-Label Classification",
author = "Gibbons, Meredith and
Mi, Maggie and
Song, Xingyi and
Villavicencio, Aline",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.261",
doi = "10.18653/v1/2024.semeval-1.261",
pages = "1860--1867",
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.",
}
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%0 Conference Proceedings
%T ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification
%A Gibbons, Meredith
%A Mi, Maggie
%A Song, Xingyi
%A Villavicencio, Aline
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F gibbons-etal-2024-shefcdteam
%X 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.
%R 10.18653/v1/2024.semeval-1.261
%U https://aclanthology.org/2024.semeval-1.261
%U https://doi.org/10.18653/v1/2024.semeval-1.261
%P 1860-1867
Markdown (Informal)
[ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification](https://aclanthology.org/2024.semeval-1.261) (Gibbons et al., SemEval 2024)
ACL