  Previous works on emotion recognition in conversation (ERC) follow a two-step
paradigm, which can be summarized as first producing context-independent
features via fine-tuning pretrained language models (PLMs) and then analyzing
contextual information and dialogue structure information among the extracted
features. However, we discover that this paradigm has several limitations.
Accordingly, we propose a novel paradigm, i.e., exploring contextual
information and dialogue structure information in the fine-tuning step, and
adapting the PLM to the ERC task in terms of input text, classification
structure, and training strategy. Furthermore, we develop our model BERT-ERC
according to the proposed paradigm, which improves ERC performance in three
aspects, namely suggestive text, fine-grained classification module, and
two-stage training. Compared to existing methods, BERT-ERC achieves substantial
improvement on four datasets, indicating its effectiveness and generalization
capability. Besides, we also set up the limited resources scenario and the
online prediction scenario to approximate real-world scenarios. Extensive
experiments demonstrate that the proposed paradigm significantly outperforms
the previous one and can be adapted to various scenes.
