Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Pattisapu Priyatam, Anish Bhanushali, Prasanna Srinivasa Murthy
Abstract
Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that explicitly account for the ordinal nature of labels. However, with the advent of Pre-trained Language Models (PLMs), it became possible to tackle ordinality through the implicit semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings.
BibTeX
@inproceedings{kasa-etal-2024-exploring,
title = "Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques",
author = "Kasa, Siva Rajesh and
Goel, Aniket and
Gupta, Karan and
Roychowdhury, Sumegh and
Priyatam, Pattisapu and
Bhanushali, Anish and
Srinivasa Murthy, Prasanna",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.320/",
doi = "10.18653/v1/2024.findings-acl.320",
pages = "5390--5404"
}