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.

自然语言处理中的序数分类（OC）是一个广泛遇到的挑战，它在各个领域，如情感分析、评级预测等中都有应用。以往的方法主要集中在修改现有或创建新的损失函数，明确考虑了标签的序数性质。然而，随着预训练语言模型的出现，通过标签的隐含语义来解决序数性变得可能。本文对这两种方法进行了全面的理论和实证研究，同时还针对具体情境提出了策略性建议，指导采用最有效的方法。

探索文本分类中的序数性：显式和隐式技术的比较研究

Exploring Ordinality in Text Classification: A Comparative Study of  Explicit and Implicit Techniques

Semantic-aware communication is a novel paradigm that draws inspiration from
human communication focusing on the delivery of the meaning of messages. It has
attracted significant interest recently due to its potential to improve the
efficiency and reliability of communication and enhance users' QoE. Most
existing works focus on transmitting and delivering the explicit semantic
meaning that can be directly identified from the source signal. This paper
investigates the implicit semantic-aware communication in which the hidden
information that cannot be directly observed from the source signal must be
recognized and interpreted by the intended users. To this end, a novel implicit
semantic-aware communication (iSAC) architecture is proposed for representing,
communicating, and interpreting the implicit semantic meaning between source
and destination users. A projection-based semantic encoder is proposed to
convert the high-dimensional graphical representation of explicit semantics
into a low-dimensional semantic constellation space for efficient physical
channel transmission. To enable the destination user to learn and imitate the
implicit semantic reasoning process of source user, a generative adversarial
imitation learning-based solution, called G-RML, is proposed. Different from
existing communication solutions, the source user in G-RML does not focus only
on sending as much of the useful messages as possible; but, instead, it tries
to guide the destination user to learn a reasoning mechanism to map any
observed explicit semantics to the corresponding implicit semantics that are
most relevant to the semantic meaning. Compared to the existing solutions, our
proposed G-RML requires much less communication and computational resources and
scales well to the scenarios involving the communication of rich semantic
meanings consisting of a large number of concepts and relations.

提出了一种新型的语义感知通信体系结构，称为 iSAC，它具有投影语义编码器和生成对抗性模仿学习等组件，旨在有效地传输、学习和解释源和目标用户之间的隐藏信息，从而提高用户的体验质量（QoE）。与现有解决方案相比，iSAC 需要更少的通信和计算资源，并且对通信丰富的语义含义和关系进行扩展缩放。