  Emotion recognition in conversation (ERC) has emerged as a research hotspot
in domains such as conversational robots and question-answer systems. How to
efficiently and adequately retrieve contextual emotional cues has been one of
the key challenges in the ERC task. Existing efforts do not fully model the
context and employ complex network structures, resulting in limited performance
gains. In this paper, we propose a novel emotion recognition network based on
curriculum learning strategy (ERNetCL). The proposed ERNetCL primarily consists
of temporal encoder (TE), spatial encoder (SE), and curriculum learning (CL)
loss. We utilize TE and SE to combine the strengths of previous methods in a
simplistic manner to efficiently capture temporal and spatial contextual
information in the conversation. To ease the harmful influence resulting from
emotion shift and simulate the way humans learn curriculum from easy to hard,
we apply the idea of CL to the ERC task to progressively optimize the network
parameters. At the beginning of training, we assign lower learning weights to
difficult samples. As the epoch increases, the learning weights for these
samples are gradually raised. Extensive experiments on four datasets exhibit
that our proposed method is effective and dramatically beats other baseline
models.
