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

TL;DR

This paper introduces BERT-ERC, a novel approach that integrates contextual and dialogue structure information directly into the fine-tuning process of BERT for emotion recognition in conversations, leading to improved performance.

Contribution

It proposes a new paradigm for ERC that incorporates dialogue context during fine-tuning, along with a specific model design and training strategy, surpassing previous methods.

Findings

BERT-ERC outperforms existing models on four datasets.

The new paradigm improves ERC accuracy significantly.

Model performs well in resource-limited and online scenarios.

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.