In real-world dialog 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 struggle to adequately incorporate both global and local contextual information, and their network structures are overly sophisticated. For this reason, we propose a simple and effective Dual-stream Recurrence-Attention Network (DualRAN), which is based on Recurrent Neural Network (RNN) and Multi-head ATtention network (MAT). DualRAN eschews the complex components of current methods and focuses on combining recurrence-based methods with attention-based ones. DualRAN is a dual-stream structure mainly consisting of local- and global-aware modules, modeling a conversation simultaneously from distinct perspectives. In addition, we develop two single-stream network variants for DualRAN, i.e., SingleRANv1 and SingleRANv2. According to the experimental findings, DualRAN boosts the weighted F1 scores by 1.43% and 0.64% on the IEMOCAP and MELD datasets, respectively, in comparison to the strongest baseline. On two other datasets (i.e., EmoryNLP and DailyDialog), our method also attains competitive results.

翻译：在真实对话系统中，理解用户情感并实现拟人化交互的能力具有重要意义。对话情感识别（ERC）是实现这一目标的关键途径之一，并日益受到关注。如何对对话中的上下文进行建模是ERC任务的核心方面和主要挑战。现有的大多数方法难以充分整合全局和局部上下文信息，且其网络结构过于复杂。为此，我们提出了一种简单有效的双流递归-注意力网络（DualRAN），该网络基于递归神经网络（RNN）和多头注意力网络（MAT）。DualRAN摒弃了当前方法中的复杂组件，专注于结合基于递归的方法和基于注意力的方法。DualRAN是一种主要由局部感知模块和全局感知模块组成的双流结构，能够从不同视角同时对对话进行建模。此外，我们还为DualRAN开发了两种单流网络变体，即SingleRANv1和SingleRANv2。实验结果表明，与最强基线相比，DualRAN在IEMOCAP和MELD数据集上分别将加权F1分数提升了1.43%和0.64%。在另外两个数据集（即EmoryNLP和DailyDialog）上，我们的方法也取得了具有竞争力的结果。