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.

本论文将 Fuzzy Fingerprints 与 RoBERTa 相结合，在 Emotion Recognition in Conversations（ERC）任务中获得了最先进的结果，并提供了更简单和可解释性更强的 Large Language Models-based 分类器。通过将发言及其以前的对话转化为上下文嵌入的发言表示，并将其提供给改进的 Fuzzy Fingerprint 分类模块，我们在广泛使用的 DailyDialog ERC 基准数据集上验证了我们的方法，获得了更轻的模型的最先进水平的结果。