Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
<|reference_start|>Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques: 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 \textbf{explicitly} account for the ordinal nature of labels. However, with the advent of Pretrained Language Models (PLMs), it became possible to tackle ordinality through the \textbf{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.<|reference_end|>
@article{kasa2024exploring,
    title={Exploring Ordinality in Text Classification: A Comparative Study of
  Explicit and Implicit Techniques},
    author={Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Anish
  Bhanushali, Nikhil Pattisapu, Prasanna Srinivasa Murthy},
    journal={arXiv preprint arXiv:2405.11775},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11775},
    primaryClass={cs.CL cs.LG}
}