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        <title>Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision</title>
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    <name type="personal">
        <namePart type="given">Yixin</namePart>
        <namePart type="family">Cao</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">Chengjiang</namePart>
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    <name type="personal">
        <namePart type="given">Xu</namePart>
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    <abstract>Jointly representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpus, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitative and quantitative, demonstrate the significance of our method.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1021</identifier>
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        <date>2018-oct&quot;-&quot;nov</date>
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%0 Conference Proceedings
%T Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
%A Cao, Yixin
%A Hou, Lei
%A Li, Juanzi
%A Liu, Zhiyuan
%A Li, Chengjiang
%A Chen, Xu
%A Dong, Tiansi
%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 cao-etal-2018-joint
%X Jointly representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpus, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitative and quantitative, demonstrate the significance of our method.
%R 10.18653/v1/D18-1021
%U https://aclanthology.org/D18-1021
%U https://doi.org/10.18653/v1/D18-1021
%P 227-237

Markdown (Informal)

[Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision](https://aclanthology.org/D18-1021) (Cao et al., EMNLP 2018)

• Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (Cao et al., EMNLP 2018)

ACL

• Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, and Tiansi Dong. 2018. Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 227–237, Brussels, Belgium. Association for Computational Linguistics.