  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. Many recent approaches show performance improvement
by combining knowledge into modules learned from external structured data.
However, structured data is difficult to access in non-English languages,
making it difficult to extend to other languages. Therefore, we extract the
pre-trained memory using the pre-trained language model as an extractor of
external knowledge. We introduce CoMPM, which combines the speaker's
pre-trained memory with the context model, and find that the pre-trained memory
significantly improves the performance of the context model. CoMPM achieves the
first or second performance on all data and is state-of-the-art among systems
that do not leverage structured data. In addition, our method shows that it can
be extended to other languages because structured knowledge is not required,
unlike previous methods. Our code is available on github
(https://github.com/rungjoo/CoMPM).
