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In real-world dialog systems, the ability to understand the user's emotionsand interact anthropomorphically is of great significance. Emotion Recognitionin Conversation (ERC) is one of the key ways to accomplish this goal and hasattracted growing attention. How to model the context in a conversation is acentral aspect and a major challenge of ERC tasks. Most existing approachesstruggle to adequately incorporate both global and local contextualinformation, and their network structures are overly sophisticated. For thisreason, we propose a simple and effective Dual-stream Recurrence-AttentionNetwork (DualRAN), which is based on Recurrent Neural Network (RNN) andMulti-head ATtention network (MAT). DualRAN eschews the complex components ofcurrent methods and focuses on combining recurrence-based methods withattention-based ones. DualRAN is a dual-stream structure mainly consisting oflocal- and global-aware modules, modeling a conversation simultaneously fromdistinct perspectives. In addition, we develop two single-stream networkvariants for DualRAN, i.e., SingleRANv1 and SingleRANv2. According to theexperimental findings, DualRAN boosts the weighted F1 scores by 1.43% and 0.64%on the IEMOCAP and MELD datasets, respectively, in comparison to the strongestbaseline. On two other datasets (i.e., EmoryNLP and DailyDialog), our methodalso attains competitive results.

A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog