Emotion Recognition in Conversations (ERC) is a critical aspect of affective computing, and it has many practical applications in healthcare, education, chatbots, and social media platforms. Earlier approaches for ERC analysis involved modeling both speaker and long-term contextual information using graph neural network architectures. However, it is ideal to deploy speaker-independent models for real-world applications. Additionally, long context windows can potentially create confusion in recognizing the emotion of an utterance in a conversation. To overcome these limitations, we propose novel line conversation graph convolutional network (LineConGCN) and graph attention (LineConGAT) models for ERC analysis. These models are speaker-independent and built using a graph construction strategy for conversations -- line conversation graphs (LineConGraphs). The conversational context in LineConGraphs is short-term -- limited to one previous and future utterance, and speaker information is not part of the graph. We evaluate the performance of our proposed models on two benchmark datasets, IEMOCAP and MELD, and show that our LineConGAT model outperforms the state-of-the-art methods with an F1-score of 64.58% and 76.50%. Moreover, we demonstrate that embedding sentiment shift information into line conversation graphs further enhances the ERC performance in the case of GCN models.

翻译：对话情感识别（ERC）是情感计算的关键组成部分，在医疗健康、教育、聊天机器人和社交媒体平台等领域具有诸多实际应用。早期的ERC分析方法利用图神经网络架构对说话者和长期上下文信息进行建模。然而，在实际应用中部署不依赖说话者的模型更为理想。此外，长上下文窗口可能对识别对话中话语的情感造成混淆。为克服这些限制，我们提出了面向ERC分析的新型对话线图卷积网络（LineConGCN）和图注意力网络（LineConGAT）模型。这些模型不依赖说话者，采用针对对话的图构建策略——对话线图（LineConGraphs）构建而成。LineConGraphs中的对话上下文为短期——仅限于前一句和后一句话语，且说话者信息不纳入图中。我们在两个基准数据集IEMOCAP和MELD上评估了所提模型的性能，结果表明我们的LineConGAT模型分别以64.58%和76.50%的F1分数超越了现有最先进方法。此外，我们证明将情感转移信息嵌入对话线图可进一步提升图卷积网络（GCN）模型在ERC任务中的表现。