Quintilian at SemEval-2023 Task 4: Grouped BERT for Multi-Label Classification

Ajay Narasimha Mopidevi, Hemanth Chenna


Abstract
In this paper, we initially discuss about the ValueEval task and the challenges involved in multi-label classification tasks. We tried to approach this task using Natural Language Inference and proposed a Grouped-BERT architecture which leverages commonality between the classes for a multi-label classification tasks.
Anthology ID:
2023.semeval-1.222
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1609–1612
Language:
URL:
https://aclanthology.org/2023.semeval-1.222
DOI:
10.18653/v1/2023.semeval-1.222
Bibkey:
Cite (ACL):
Ajay Narasimha Mopidevi and Hemanth Chenna. 2023. Quintilian at SemEval-2023 Task 4: Grouped BERT for Multi-Label Classification. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1609–1612, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Quintilian at SemEval-2023 Task 4: Grouped BERT for Multi-Label Classification (Mopidevi & Chenna, SemEval 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.semeval-1.222.pdf