BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation

• URL: http://arxiv.org/abs/2301.06745v1
• Date: Tue, 17 Jan 2023 08:03:32 GMT
• Title: BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation
• Authors: Xiangyu Qin, Zhiyu Wu, Jinshi Cui, Tingting Zhang, Yanran Li, Jian Luan, Bin Wang, Li Wang
• Abstract summary: Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm. We propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step. We develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects.
• Score: 19.663265448700002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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