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        <title>Adversarial Multi-lingual Neural Relation Extraction</title>
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    <name type="personal">
        <namePart type="given">Xiaozhi</namePart>
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        <namePart type="given">Xu</namePart>
        <namePart type="family">Han</namePart>
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        <namePart type="given">Yankai</namePart>
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    <name type="personal">
        <namePart type="given">Zhiyuan</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Maosong</namePart>
        <namePart type="family">Sun</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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    <abstract>Multi-lingual relation extraction aims to find unknown relational facts from text in various languages. Existing models cannot well capture the consistency and diversity of relation patterns in different languages. To address these issues, we propose an adversarial multi-lingual neural relation extraction (AMNRE) model, which builds both consistent and individual representations for each sentence to consider the consistency and diversity among languages. Further, we adopt an adversarial training strategy to ensure those consistent sentence representations could effectively extract the language-consistent relation patterns. The experimental results on real-world datasets demonstrate that our AMNRE model significantly outperforms the state-of-the-art models. The source code of this paper can be obtained from https://github.com/thunlp/AMNRE.</abstract>
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%0 Conference Proceedings
%T Adversarial Multi-lingual Neural Relation Extraction
%A Wang, Xiaozhi
%A Han, Xu
%A Lin, Yankai
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wang-etal-2018-adversarial
%X Multi-lingual relation extraction aims to find unknown relational facts from text in various languages. Existing models cannot well capture the consistency and diversity of relation patterns in different languages. To address these issues, we propose an adversarial multi-lingual neural relation extraction (AMNRE) model, which builds both consistent and individual representations for each sentence to consider the consistency and diversity among languages. Further, we adopt an adversarial training strategy to ensure those consistent sentence representations could effectively extract the language-consistent relation patterns. The experimental results on real-world datasets demonstrate that our AMNRE model significantly outperforms the state-of-the-art models. The source code of this paper can be obtained from https://github.com/thunlp/AMNRE.
%U https://aclanthology.org/C18-1099
%P 1156-1166

Markdown (Informal)

[Adversarial Multi-lingual Neural Relation Extraction](https://aclanthology.org/C18-1099) (Wang et al., COLING 2018)

• Adversarial Multi-lingual Neural Relation Extraction (Wang et al., COLING 2018)

ACL

• Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2018. Adversarial Multi-lingual Neural Relation Extraction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1156–1166, Santa Fe, New Mexico, USA. Association for Computational Linguistics.