Affect modeling is viewed, traditionally, as the process of mapping
measurable affect manifestations from multiple modalities of user input to
affect labels. That mapping is usually inferred through end-to-end
(manifestation-to-affect) machine learning processes. What if, instead, one
trains general, subject-invariant representations that consider affect
information and then uses such representations to model affect? In this paper
we assume that affect labels form an integral part, and not just the training
signal, of an affect representation and we explore how the recent paradigm of
contrastive learning can be employed to discover general high-level
affect-infused representations for the purpose of modeling affect. We introduce
three different supervised contrastive learning approaches for training
representations that consider affect information. In this initial study we test
the proposed methods for arousal prediction in the RECOLA dataset based on user
information from multiple modalities. Results demonstrate the representation
capacity of contrastive learning and its efficiency in boosting the accuracy of
affect models. Beyond their evidenced higher performance compared to end-to-end
arousal classification, the resulting representations are general-purpose and
subject-agnostic, as training is guided though general affect information
available in any multimodal corpus.

通过对多模态用户输入的可测情感表征进行情感分类，并通过对比学习来训练泛化的、高水平的、融入情感信息的情感表示，提高情感模型的准确性。

监督式对比学习用于情感建模

Supervised Contrastive Learning for Affect Modelling

To handle emotional expressions in computer applications, Russell's circum-
plex model has been useful for representing emotions according to valence and
arousal. In SentiWordNet, the level of valence is automatically assigned to a
large number of synsets (groups of synonyms in WordNet) using semi-supervised
learning. However, when assigning the level of arousal, the existing method
proposed for SentiWordNet reduces the accuracy of sentiment prediction. In this
paper, we propose a block-segmentation vector for predicting the arousal levels
of many synsets from a small number of labeled words using semi-supervised
learning. We analyze the distribution of arousal and non-arousal words in a
corpus of sentences by comparing it with the distribution of valence words. We
address the problem that arousal level prediction fails when arousal and
non-arousal words are mixed together in some sentences. To capture the features
of such arousal and non-arousal words, we generate word vectors based on
inverted indexes by block IDs, where the corpus is divided into blocks in the
flow of sentences. In the evaluation experiment, we show that the results of
arousal prediction with the block-segmentation vectors outperform the results
of the previous method in SentiWordNet.

本文提出了一种基于块分割矢量的方法来预测词的情感唤起水平，使用半监督学习从少量标记词中学习，并解决了情感和非情感单词混合的问题。实验表明，与 SentiWordNet 中的先前方法相比，使用块分割向量进行的唤起水平预测结果更好。