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        <title>End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network</title>
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        <namePart type="given">Ying</namePart>
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    <name type="personal">
        <namePart type="given">Caicong</namePart>
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    <name type="personal">
        <namePart type="given">Xiaoqiang</namePart>
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    <abstract>Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.</abstract>
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%0 Conference Proceedings
%T End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network
%A Chen, Ying
%A Hou, Wenjun
%A Li, Shoushan
%A Wu, Caicong
%A Zhang, Xiaoqiang
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F chen-etal-2020-end
%X Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.
%R 10.18653/v1/2020.coling-main.17
%U https://aclanthology.org/2020.coling-main.17
%U https://doi.org/10.18653/v1/2020.coling-main.17
%P 198-207

Markdown (Informal)

[End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network](https://aclanthology.org/2020.coling-main.17) (Chen et al., COLING 2020)

• End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (Chen et al., COLING 2020)

ACL

• Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, and Xiaoqiang Zhang. 2020. End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network. In Proceedings of the 28th International Conference on Computational Linguistics, pages 198–207, Barcelona, Spain (Online). International Committee on Computational Linguistics.