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- W4386766915 abstract "Emotion causes constitute a pivotal component in the comprehension of emotional conversations. Recently, a new task named Causal Emotion Entailment (CEE) has been proposed to identify the causal utterances for the target emotional utterance in a conversation. Although researchers have achieved some progress in solving this problem, they failed to adequately incorporate speaker characteristics and overlooked the effects of temporal relations in conversation structures. To fill such a research gap to some extent, we propose a novel causal emotion entailment framework, namely MPEG (Multi-Perspective Enhanced Graph attention network). The training of MPEG consists of three stages. Firstly, we utilize a speaker-aware pre-trained model and two attention mechanisms to obtain the utterance representations that incorporate local contexts as well as the speaker and emotional information. Then, these representations are fed into a graph attention network to model the conversation structures and emotional dynamics from both local and global perspectives. Finally, a fully-connected network is implemented to predict the relationships between emotional utterances and causal utterances. Experimental results show that MPEG achieves state-of-the-art performance. The source code is available at <uri xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>https://github.com/slptongji/MPEG</uri> ." @default.
- W4386766915 created "2023-09-16" @default.
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- W4386766915 date "2023-01-01" @default.
- W4386766915 modified "2023-09-27" @default.
- W4386766915 title "MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in Conversations" @default.
- W4386766915 doi "https://doi.org/10.1109/taffc.2023.3315752" @default.
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