Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation

TL;DR

This paper introduces a novel speaker-guided encoder-decoder framework for emotion recognition in conversations, effectively modeling dynamic intra- and inter-speaker dependencies to improve emotion prediction accuracy.

Contribution

The paper proposes a flexible, scalable framework that jointly explores speaker dependencies and leverages speaker information for emotion decoding in conversations.

Findings

SGED outperforms existing methods in emotion recognition accuracy.

The framework demonstrates high scalability with different context encoders.

Experimental results confirm the effectiveness of joint speaker dependency modeling.

Abstract

The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a novel speaker modeling scheme that explores intra- and inter-speaker dependencies jointly in a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED) framework for ERC, which fully exploits speaker information for the decoding of emotion. We use different existing methods as the conversational context encoder of our framework, showing the high scalability and flexibility of the proposed framework. Experimental results demonstrate the superiority and effectiveness of SGED.