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.

翻译：序数分类（OC）是自然语言处理（NLP）中广泛面临的挑战，在情感分析、评分预测等多个领域均有应用。此前应对OC的方法主要侧重于修改现有损失函数或创建新型损失函数，以**显式**地考虑标签的序数性质。然而，随着预训练语言模型（PLMs）的出现，通过标签的**隐式**语义处理序数性也成为可能。本文从理论与实证两个角度对这两种方法进行了全面考察。此外，我们还就基于特定场景应采纳的最有效策略提出了战略性建议。