Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations

TL;DR

This paper combines interpretability techniques with large language models to improve emotion recognition in conversations, achieving state-of-the-art results with a simpler, more explainable classifier.

Contribution

It introduces a novel approach that integrates Fuzzy Fingerprints with RoBERTa embeddings for emotion recognition, enhancing interpretability without sacrificing performance.

Findings

Achieved state-of-the-art results on DailyDialog dataset

Used a lighter model compared to traditional large language models

Enhanced interpretability of emotion recognition models

Abstract

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.