Fuzzy Fingerprints have been successfully used as an interpretable text classification technique, but, like most other techniques, have been largely surpassed in performance by Large Pre-trained Language Models, such as BERT or RoBERTa. These models deliver state-of-the-art results in several Natural Language Processing tasks, namely Emotion Recognition in Conversations (ERC), but suffer from the lack of interpretability and explainability. In this paper, we propose to combine the two approaches to perform ERC, as a means to obtain simpler and more interpretable Large Language Models-based classifiers. We propose to feed the utterances and their previous conversational turns to a pre-trained RoBERTa, obtaining contextual embedding utterance representations, that are then supplied to an adapted Fuzzy Fingerprint classification module. We validate our approach on the widely used DailyDialog ERC benchmark dataset, in which we obtain state-of-the-art level results using a much lighter model.

翻译：模糊指纹作为一种可解释的文本分类技术曾成功应用，但如同大多数其他技术一样，其在性能上已被大型预训练语言模型（如BERT或RoBERTa）大幅超越。这些模型在多项自然语言处理任务（特别是对话情感识别）中取得了最先进的结果，但却缺乏可解释性与可说明性。本文提出将两种方法相结合以进行对话情感识别，旨在获得更简单且更具可解释性的大型语言模型分类器。我们将话语及其先前对话轮次输入预训练的RoBERTa模型，获取上下文嵌入话语表征，再将其输入经改进的模糊指纹分类模块。我们在广泛使用的对话情感识别基准数据集DailyDialog上验证了该方法，使用更轻量级的模型即可获得最先进水平的结果。