Abstract
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.
Abstract (translated)
深度学习模型的出现对实现对话中情感识别（ERC）做出了显著的贡献。然而，由于人类情感的多样性和主观性，这项任务仍然是一个重要的挑战。以前的工作主要使用基于图的对话表示来构建预测模型。在这项工作中，我们提出了一种将对话上下文建模为元学习训练策略的方法，包括两个步骤。这使我们能够在灵活的分类场景中执行ERC，并实现了一个轻量级但高效的模型。通过Siamese网络架构进行元学习，我们在DailyDialog数据集上取得了57.71的宏观F1分数的 emotion分类，超过了相关研究。这种最先进的结果在关于使用元学习进行情感识别方面具有前景，然而与微F1分数相比还有待提高。
URL
https://arxiv.org/abs/2404.11141
