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arXiv · 1811.10776

Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

Yixin Cao·Lei Hou·Juanzi Li·Zhiyuan Liu·Chengjiang Li·Xu Chen·Tiansi Dong
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Abstract

Joint 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 corpora, 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 qualitatively and quantitatively, demonstrate the significance of our method.

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Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong. 2018-11-27. Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision. https://arxiv.org/abs/1811.10776

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