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    <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.</abstract>
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%0 Conference Proceedings
%T Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
%A Kasa, Siva Rajesh
%A Goel, Aniket
%A Gupta, Karan
%A Roychowdhury, Sumegh
%A Priyatam, Pattisapu
%A Bhanushali, Anish
%A Srinivasa Murthy, Prasanna
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F kasa-etal-2024-exploring
%X 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.
%U https://aclanthology.org/2024.findings-acl.320
%P 5390-5404

Markdown (Informal)

[Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques](https://aclanthology.org/2024.findings-acl.320) (Kasa et al., Findings 2024)

• Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques (Kasa et al., Findings 2024)

ACL

• Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Pattisapu Priyatam, Anish Bhanushali, and Prasanna Srinivasa Murthy. 2024. Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques. In Findings of the Association for Computational Linguistics ACL 2024, pages 5390–5404, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.