  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.
