Proxy-based Metric Learning for Emotion Recognition

Junhyeong Park, Geonsik Youn, Bohan Yoon, Byeonghun Kim, J. Rhee
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Abstract

Emotion Recognition (ER) is an essential research area of natural language processing that can be applied to various fields. Texts in the fields of health care, marketing, and psychological counseling take various forms, and it is very important from a business point of view to find the emotions inherent in these texts. Recently, ER using text embeddings generated through a pre-trained language model with a large corpus was performed. However, since the embeddings are generalized to various domains, there is a limitation to directly using them for ER. In this study, to overcome the limitation, we propose a method that modifies generalized embeddings to emotional embeddings by performing proxy-based metric learning. In the proposed method, we fine-tuned the pre-trained language model by using proxy-anchor loss so that embeddings represent emotion appropriately. Previous studies only added linear classifiers. But, it is possible to capture emotional relationships between data by using proxy-based metric learning. In this study, we conducted ER experiments with benchmark datasets. The experimental result shows that the proposed method achieves better performance than the baseline and creates emotion-specific embeddings.
基于代理的情感识别度量学习
情感识别是自然语言处理的一个重要研究领域,可以应用于各个领域。医疗保健、市场营销和心理咨询领域的文本形式多种多样,从商业的角度来看,找到这些文本中固有的情感是非常重要的。最近,研究人员利用一个预训练的语言模型和一个大型语料库生成的文本嵌入来进行ER。然而,由于嵌入被推广到各个领域,因此直接将其用于ER存在局限性。在本研究中,为了克服这种局限性,我们提出了一种通过执行基于代理的度量学习将广义嵌入修改为情感嵌入的方法。在提出的方法中,我们通过使用代理锚点损失对预训练的语言模型进行微调,使嵌入适当地表示情感。以前的研究只增加了线性分类器。但是,通过使用基于代理的度量学习,可以捕获数据之间的情感关系。在本研究中,我们对基准数据集进行了ER实验。实验结果表明,该方法比基线方法具有更好的性能,并能生成特定情感的嵌入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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