@inproceedings{cao-etal-2018-joint,
title = "Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision",
author = "Cao, Yixin and
Hou, Lei and
Li, Juanzi and
Liu, Zhiyuan and
Li, Chengjiang and
Chen, Xu and
Dong, Tiansi",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1021",
doi = "10.18653/v1/D18-1021",
pages = "227--237",
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.",
}
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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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%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
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%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)
ACL