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        <title>Adversarial Feature Adaptation for Cross-lingual Relation Classification</title>
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        <namePart type="given">Bowei</namePart>
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        <namePart type="given">Zengzhuang</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>Relation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most existing methods focus on exploiting mono-lingual data, e.g., in English, due to the lack of annotated data in other languages. In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data. Such a feature adaptation approach enables feature imitation via the competition between a relation classification network and a rival discriminator. Experimental results on the ACE 2005 multilingual training corpus, treating English as the source language and Chinese the target, demonstrate the effectiveness of our proposed approach, yielding an improvement of 5.7% over the state-of-the-art.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Adversarial Feature Adaptation for Cross-lingual Relation Classification
%A Zou, Bowei
%A Xu, Zengzhuang
%A Hong, Yu
%A Zhou, Guodong
%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 zou-etal-2018-adversarial
%X Relation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most existing methods focus on exploiting mono-lingual data, e.g., in English, due to the lack of annotated data in other languages. In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data. Such a feature adaptation approach enables feature imitation via the competition between a relation classification network and a rival discriminator. Experimental results on the ACE 2005 multilingual training corpus, treating English as the source language and Chinese the target, demonstrate the effectiveness of our proposed approach, yielding an improvement of 5.7% over the state-of-the-art.
%U https://aclanthology.org/C18-1037
%P 437-448

Markdown (Informal)

[Adversarial Feature Adaptation for Cross-lingual Relation Classification](https://aclanthology.org/C18-1037) (Zou et al., COLING 2018)

• Adversarial Feature Adaptation for Cross-lingual Relation Classification (Zou et al., COLING 2018)

ACL

• Bowei Zou, Zengzhuang Xu, Yu Hong, and Guodong Zhou. 2018. Adversarial Feature Adaptation for Cross-lingual Relation Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 437–448, Santa Fe, New Mexico, USA. Association for Computational Linguistics.