CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation

Anonymous

Abstract: As the use of interactive machines grow, the task of Emotion Recognition in Conversation (ERC) became more important. If the machine generated sentences reflect emotion, more human-like sympathetic conversations are possible. Since emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, many studies reflect the dialogue context to improve the performances. We introduce CoMPM, a context embedding module (CoM) combined with a pre-trained memory module (PM) that tracks memory of the speaker's previous utterances within the context, and show that the pre-trained memory significantly improves the final accuracy of emotion recognition. We achieve competitive performance with previous methods on English datasets (MELD, EmoryNLP, IEMOCAP, DailyDailog), and achieve good performance with small data sets. In addition, our method shows that it can be extended to other languages because structured knowledge is not required unlike existing methods.