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Dual Graph Convolutional Networks for Document-Level Event Causality Identification

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Web and Big Data (APWeb-WAIM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13422))

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Abstract

Event causalities organize events into a graph according to causal logics, which assists humans in decision making by causal reasoning among events. Despite many efforts to identify event causalities, most of them assume that only one causality exists in a sentence or causalities only occur in adjacent sentences, leading to the incapability of detecting multiple causalities or document-level causalities. In this paper, we propose a novel model for document-level event causality identification named DocECI. We define two heterogeneous document graphs, namely text structure graph and mention relation graph, and encode them with relational graph convolutional networks, which gradually aggregate the information of multi-granular nodes in a cascade manner and capture the causality patterns. Experiments on a benchmark dataset show that DocECI outperforms existing models by a significant margin. Moreover, a new experiment is conducted on causality direction identification, which is overlooked by existing models.

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Notes

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Acknowledgments

This work is supported by Science and Technology on Information Systems Engineering Laboratory (No. 05202006).

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China

    Yang Liu, Wenzheng Zhao & Wei Hu

  2. Science and Technology on Information Systems Engineering Laboratory, Nanjing, Jiangsu, China

    Yang Liu, Xiaoxia Jiang & Weiyi Ge

Authors
  1. Yang Liu
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  2. Xiaoxia Jiang
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  3. Wenzheng Zhao
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  4. Weiyi Ge
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  5. Wei Hu
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Corresponding author

Correspondence to Wei Hu .

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Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. Newcastle University, Callaghan, NSW, Australia

    Lin Yue

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Chuanqi Tao

  4. Jinan University, Guangzhou, China

    Xuming Han

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Diego Calvanese

  6. University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa

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Liu, Y., Jiang, X., Zhao, W., Ge, W., Hu, W. (2023). Dual Graph Convolutional Networks for Document-Level Event Causality Identification. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_9

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  • DOI: https://doi.org/10.1007/978-3-031-25198-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25197-9

  • Online ISBN: 978-3-031-25198-6

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