Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition
  in Conversations
Patrícia Pereira Rui Ribeiro Helena Moniz Luisa Coheur Joao Paulo Carvalho
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.