  The advent of deep learning models has made a considerable contribution to
the achievement of Emotion Recognition in Conversation (ERC). However, this
task still remains an important challenge due to the plurality and subjectivity
of human emotions. Previous work on ERC provides predictive models using mostly
graph-based conversation representations. In this work, we propose a way to
model the conversational context that we incorporate into a metric learning
training strategy, with a two-step process. This allows us to perform ERC in a
flexible classification scenario and to end up with a lightweight yet efficient
model. Using metric learning through a Siamese Network architecture, we achieve
57.71 in macro F1 score for emotion classification in conversation on
DailyDialog dataset, which outperforms the related work. This state-of-the-art
result is promising regarding the use of metric learning for emotion
recognition, yet perfectible compared to the microF1 score obtained.
