Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions
This work addresses the need for emotion-aware dialogue systems by providing a novel annotated corpus, though it is incremental as it builds on existing emotion recognition research.
The authors tackled the problem of developing emotion-aware dialogue systems by creating a Japanese Twitter dialogue corpus annotated with both expressed and experienced emotions, revealing differences between them and showing that multi-task learning improves recognition, with experiments indicating the difficulty of recognizing experienced emotions.
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.