2023/09/08 by Patrícia Pereira, Pereira, Patrícia, Rui Ribeiro +9 · 1 voice · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Artificial intelligence #Benchmark (surveying) #Computation and Language (cs.CL) #Computer science #Embedding #Engineering #FOS: Computer and information sciences #Fuzzy logic #Interpretability #Language model #Machine learning #Natural language processing #Sentiment Analysis and Opinion Mining #Speech recognition #Text and Document Classification Technologies #Topic Modeling #Transformer #Utterance #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.2309.04292
openalex publication_date 2023/09/08 · arxiv published 2023/09/08 · arxiv updated 2023/09/08 · openalex created_date 2023/09/12 · openalex updated_date 2026/07/28
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.