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        <title>Exploiting Contextual Information via Dynamic Memory Network for Event Detection</title>
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        <namePart type="given">Shaobo</namePart>
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    <name type="personal">
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    <abstract>The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best F1 score compared to the state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1127</identifier>
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        <date>2018-oct&quot;-&quot;nov</date>
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%0 Conference Proceedings
%T Exploiting Contextual Information via Dynamic Memory Network for Event Detection
%A Liu, Shaobo
%A Cheng, Rui
%A Yu, Xiaoming
%A Cheng, Xueqi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct" "nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F liu-etal-2018-exploiting-contextual
%X The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best F1 score compared to the state-of-the-art methods.
%R 10.18653/v1/D18-1127
%U https://aclanthology.org/D18-1127
%U https://doi.org/10.18653/v1/D18-1127
%P 1030-1035

Markdown (Informal)

[Exploiting Contextual Information via Dynamic Memory Network for Event Detection](https://aclanthology.org/D18-1127) (Liu et al., EMNLP 2018)

• Exploiting Contextual Information via Dynamic Memory Network for Event Detection (Liu et al., EMNLP 2018)

ACL

• Shaobo Liu, Rui Cheng, Xiaoming Yu, and Xueqi Cheng. 2018. Exploiting Contextual Information via Dynamic Memory Network for Event Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1030–1035, Brussels, Belgium. Association for Computational Linguistics.