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        <title>Heterogeneous Graph Neural Networks to Predict What Happen Next</title>
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        <namePart type="given">Jianming</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Honghui</namePart>
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    <abstract>Given an incomplete event chain, script learning aims to predict the missing event, which can support a series of NLP applications. Existing work cannot well represent the heterogeneous relations and capture the discontinuous event segments that are common in the event chain. To address these issues, we introduce a heterogeneous-event (HeterEvent) graph network. In particular, we employ each unique word and individual event as nodes in the graph, and explore three kinds of edges based on realistic relations (e.g., the relations of word-and-word, word-and-event, event-and-event). We also design a message passing process to realize information interactions among homo or heterogeneous nodes. And the discontinuous event segments could be explicitly modeled by finding the specific path between corresponding nodes in the graph. The experimental results on one-step and multi-step inference tasks demonstrate that our ensemble model HeterEvent[W+E] can outperform existing baselines.</abstract>
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%0 Conference Proceedings
%T Heterogeneous Graph Neural Networks to Predict What Happen Next
%A Zheng, Jianming
%A Cai, Fei
%A Ling, Yanxiang
%A Chen, Honghui
%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 zheng-etal-2020-heterogeneous
%X Given an incomplete event chain, script learning aims to predict the missing event, which can support a series of NLP applications. Existing work cannot well represent the heterogeneous relations and capture the discontinuous event segments that are common in the event chain. To address these issues, we introduce a heterogeneous-event (HeterEvent) graph network. In particular, we employ each unique word and individual event as nodes in the graph, and explore three kinds of edges based on realistic relations (e.g., the relations of word-and-word, word-and-event, event-and-event). We also design a message passing process to realize information interactions among homo or heterogeneous nodes. And the discontinuous event segments could be explicitly modeled by finding the specific path between corresponding nodes in the graph. The experimental results on one-step and multi-step inference tasks demonstrate that our ensemble model HeterEvent[W+E] can outperform existing baselines.
%R 10.18653/v1/2020.coling-main.29
%U https://aclanthology.org/2020.coling-main.29
%U https://doi.org/10.18653/v1/2020.coling-main.29
%P 328-338

Markdown (Informal)

[Heterogeneous Graph Neural Networks to Predict What Happen Next](https://aclanthology.org/2020.coling-main.29) (Zheng et al., COLING 2020)

• Heterogeneous Graph Neural Networks to Predict What Happen Next (Zheng et al., COLING 2020)

ACL

• Jianming Zheng, Fei Cai, Yanxiang Ling, and Honghui Chen. 2020. Heterogeneous Graph Neural Networks to Predict What Happen Next. In Proceedings of the 28th International Conference on Computational Linguistics, pages 328–338, Barcelona, Spain (Online). International Committee on Computational Linguistics.