DetectiveNN: Imitating Human Emotional Reasoning with a Recall-Detect-Predict Framework for Emotion Recognition in Conversations

Anonymous

Abstract: Recognizing emotions in conversations involves an internal cognitive process that interprets emotional cues by using a collection of past emotional experiences. However, many existing methods struggle to decipher emotional cues in dialogues due to their models' lack of capacity for cognitive reasoning. In this work, we introduce an innovative Detective Network (DetectiveNN), a novel model that is grounded in the cognitive theory of emotion and utilizes a "recall-detect-predict" framework to imitate human emotional reasoning. This process begins by `recalling' past interactions of a specific speaker to collect emotional cues. It then `detects' relevant emotional patterns by interpreting these cues in the context of the ongoing conversation. Finally, it `predicts' the speaker's emotional state in the next moment. Tested on three benchmark datasets, our approach outperforms existing methods. This highlights the advantages of incorporating cognitive factors into deep learning, enhancing task efficiency and prediction accuracy.

Paper Type: long

Research Area: NLP Applications

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings

Languages Studied: English