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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