Directed Acyclic Graph Network for Conversational Emotion Recognition

• URL: http://arxiv.org/abs/2105.12907v1
• Date: Thu, 27 May 2021 01:51:37 GMT
• Title: Directed Acyclic Graph Network for Conversational Emotion Recognition
• Authors: Weizhou Shen, Siyue Wu, Yunyi Yang and Xiaojun Quan
• Abstract summary: We propose a novel idea of encoding utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation. DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison.
• Score: 12.191046814462853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network,~namely DAG-ERC, to implement this idea.~In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models,~DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context.~Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison.~The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.

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