Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions

TL;DR

This paper introduces a Japanese dialogue corpus annotated with expressed and experienced emotions, aiming to improve emotion recognition and response generation in dialogue systems.

Contribution

It presents a novel method for annotating dialogue with two types of emotions and demonstrates its utility through experiments and statistical analysis.

Findings

Recognizing experienced emotions is challenging.

Multi-task learning improves emotion recognition accuracy.

The corpus reveals differences between expressed and experienced emotions.

Abstract

In communication, a human would recognize the emotion of an interlocutor and respond with an appropriate emotion, such as empathy and comfort. Toward developing a dialogue system with such a human-like ability, we propose a method to build a dialogue corpus annotated with two kinds of emotions. We collect dialogues from Twitter and annotate each utterance with the emotion that a speaker put into the utterance (expressed emotion) and the emotion that a listener felt after listening to the utterance (experienced emotion). We built a dialogue corpus in Japanese using this method, and its statistical analysis revealed the differences between expressed and experienced emotions. We conducted experiments on recognition of the two kinds of emotions. The experimental results indicated the difficulty in recognizing experienced emotions and the effectiveness of multi-task learning of the two kinds of emotions. We hope that the constructed corpus will facilitate the study on emotion recognition in a dialogue and emotion-aware dialogue response generation.