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        <title>Document-Level Event Factuality Identification via Adversarial Neural Network</title>
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        <namePart type="given">Zhong</namePart>
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        <namePart type="given">Peifeng</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Qiaoming</namePart>
        <namePart type="family">Zhu</namePart>
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    <name type="personal">
        <namePart type="given">Guodong</namePart>
        <namePart type="family">Zhou</namePart>
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            <namePart type="given">Jill</namePart>
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    <abstract>Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP). Previous studies mainly suffer from the scarcity of suitable corpus and effective methods. To solve these two issues, we first construct a corpus annotated with both document- and sentence-level event factuality information on both English and Chinese texts. Then we present an LSTM neural network based on adversarial training with both intra- and inter-sequence attentions to identify document-level event factuality. Experimental results show that our neural network model can outperform various baselines on the constructed corpus.</abstract>
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%0 Conference Proceedings
%T Document-Level Event Factuality Identification via Adversarial Neural Network
%A Qian, Zhong
%A Li, Peifeng
%A Zhu, Qiaoming
%A Zhou, Guodong
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F qian-etal-2019-document
%X Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP). Previous studies mainly suffer from the scarcity of suitable corpus and effective methods. To solve these two issues, we first construct a corpus annotated with both document- and sentence-level event factuality information on both English and Chinese texts. Then we present an LSTM neural network based on adversarial training with both intra- and inter-sequence attentions to identify document-level event factuality. Experimental results show that our neural network model can outperform various baselines on the constructed corpus.
%R 10.18653/v1/N19-1287
%U https://aclanthology.org/N19-1287
%U https://doi.org/10.18653/v1/N19-1287
%P 2799-2809

Markdown (Informal)

[Document-Level Event Factuality Identification via Adversarial Neural Network](https://aclanthology.org/N19-1287) (Qian et al., NAACL 2019)

• Document-Level Event Factuality Identification via Adversarial Neural Network (Qian et al., NAACL 2019)

ACL

• Zhong Qian, Peifeng Li, Qiaoming Zhu, and Guodong Zhou. 2019. Document-Level Event Factuality Identification via Adversarial Neural Network. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2799–2809, Minneapolis, Minnesota. Association for Computational Linguistics.