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        <title>Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism</title>
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    <name type="personal">
        <namePart type="given">Shirong</namePart>
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    <name type="personal">
        <namePart type="given">Guilin</namePart>
        <namePart type="family">Qi</namePart>
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    <name type="personal">
        <namePart type="given">Lusheng</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Event extraction plays an important role in legal applications, including case push and auxiliary judgment. However, traditional event structure cannot express the connections between arguments, which are extremely important in legal events. Therefore, this paper defines a dynamic event structure for Chinese legal events. To distinguish between similar events, we design hierarchical event features for event detection. Moreover, to address the problem of long-distance semantic dependence and anaphora resolution in argument classification, we propose a novel pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. We label a Chinese legal event dataset and evaluate our model on it. Experimental results demonstrate that our model can surpass other state-of-the-art models.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.9</identifier>
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%0 Conference Proceedings
%T Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism
%A Shen, Shirong
%A Qi, Guilin
%A Li, Zhen
%A Bi, Sheng
%A Wang, Lusheng
%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 shen-etal-2020-hierarchical
%X Event extraction plays an important role in legal applications, including case push and auxiliary judgment. However, traditional event structure cannot express the connections between arguments, which are extremely important in legal events. Therefore, this paper defines a dynamic event structure for Chinese legal events. To distinguish between similar events, we design hierarchical event features for event detection. Moreover, to address the problem of long-distance semantic dependence and anaphora resolution in argument classification, we propose a novel pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. We label a Chinese legal event dataset and evaluate our model on it. Experimental results demonstrate that our model can surpass other state-of-the-art models.
%R 10.18653/v1/2020.coling-main.9
%U https://aclanthology.org/2020.coling-main.9
%U https://doi.org/10.18653/v1/2020.coling-main.9
%P 100-113

Markdown (Informal)

[Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism](https://aclanthology.org/2020.coling-main.9) (Shen et al., COLING 2020)

• Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism (Shen et al., COLING 2020)

ACL

• Shirong Shen, Guilin Qi, Zhen Li, Sheng Bi, and Lusheng Wang. 2020. Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism. In Proceedings of the 28th International Conference on Computational Linguistics, pages 100–113, Barcelona, Spain (Online). International Committee on Computational Linguistics.