Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
This work addresses ordinal classification for NLP applications like sentiment analysis, but it is incremental as it compares existing approaches.
The paper tackled the problem of ordinal classification in NLP by comparing explicit and implicit techniques, finding that implicit methods using pretrained language models can be effective but depend on specific settings.
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.