In real-world dialogue systems, the ability to understand the user's emotions
and interact anthropomorphically is of great significance. Emotion Recognition
in Conversation (ERC) is one of the key ways to accomplish this goal and has
attracted growing attention. How to model the context in a conversation is a
central aspect and a major challenge of ERC tasks. Most existing approaches are
generally unable to capture both global and local contextual information
efficiently, and their network structures are too complex to design. For this
reason, in this work, we propose a straightforward Dual-stream
Recurrence-Attention Network (DualRAN) based on Recurrent Neural Network (RNN)
and Multi-head ATtention network (MAT). The proposed model eschews the complex
network structure of current methods and focuses on combining recurrence-based
methods with attention-based methods. DualRAN is a dual-stream structure mainly
consisting of local- and global-aware modules, modeling a conversation from
distinct perspectives. To achieve the local-aware module, we extend the
structure of RNN, thus enhancing the expressive capability of the network. In
addition, we develop two single-stream network variants for DualRAN, i.e.,
SingleRANv1 and SingleRANv2. We conduct extensive experiments on four widely
used benchmark datasets, and the results reveal that the proposed model
outshines all baselines. Ablation studies further demonstrate the effectiveness
of each component.

本文提出一种基于双重 RNN 和多头注意力网络（MAT）的直观双流递归关注网络（DualRAN），该模型旨在解决情感对话（ERC）任务中建模上下文的难题，能够更有效地捕获全局和局部背景信息。实验结果表明 DualRAN 模型优于所有基线，并且每个组件的有效性得到了深入的证明。