Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations
This work addresses interpretability issues in emotion recognition for conversational AI, though it is incremental as it hybridizes existing methods.
The paper tackles the problem of lack of interpretability in large language models for emotion recognition in conversations by combining RoBERTa with fuzzy fingerprints, achieving state-of-the-art results on the DailyDialog benchmark with a lighter model.
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.