Example-based Emotion Recognition in Conversations

Abstract

Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used to analyze conversations conducted via social media and build emotional and empathetic dialogue systems. In ERC, some utterances with the same surface can show different emotions depending on the conversational context. A typical solution to this issue is to encode contextual information by concatenating a series of utterances and inputting them into a classifier model. In this paper, we propose a method to incorporate an external database to the classifier model. Given a target utterance, we search the training dataset for utterances that are semantically similar to the target. The retrieved utterances are used to calculate probability distribution on emotion labels. The distribution is combined with another probability distribution from the classifier model by using a weighted linear summation. Furthermore, in combining the distributions, we propose dynamic weight coefficients depending on target utterances instead of a constant coefficient. Our experimental results on three ERC datasets show that our method performs best over the baselines.