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.

翻译：深度学习模型的出现对对话情感识别（ERC）的进展做出了显著贡献。然而，由于人类情感的多样性和主观性，这一任务仍然面临重要挑战。现有ERC研究主要采用基于图的对话表示构建预测模型。本文提出了一种对话上下文建模方法，将其融入度量学习训练策略中，通过两阶段处理流程实现灵活分类场景下的ERC，最终获得轻量且高效的模型。基于孪生网络架构的度量学习方法，我们在DailyDialog数据集上实现了57.71的宏F1分数，在对话情感分类任务中超越相关研究。这一先进结果展示了度量学习在情感识别领域的应用前景，但相较于加权F1分数仍存在优化空间。