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    <titleInfo>
        <title>Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping</title>
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    <name type="personal">
        <namePart type="given">Jian</namePart>
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    <name type="personal">
        <namePart type="given">Radu</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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            <namePart type="given">Kentaro</namePart>
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        <name type="personal">
            <namePart type="given">Jing</namePart>
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            <namePart type="given">Xiaojun</namePart>
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    <abstract>Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challenging to transfer an RE model of a resource-rich language to a resource-poor language. In this paper, we propose a new approach for cross-lingual RE model transfer based on bilingual word embedding mapping. It projects word embeddings from a target language to a source language, so that a well-trained source-language neural network RE model can be directly applied to the target language. Experiment results show that the proposed approach achieves very good performance for a number of target languages on both in-house and open datasets, using a small bilingual dictionary with only 1K word pairs.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1038</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
%A Ni, Jian
%A Florian, Radu
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ni-florian-2019-neural
%X Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challenging to transfer an RE model of a resource-rich language to a resource-poor language. In this paper, we propose a new approach for cross-lingual RE model transfer based on bilingual word embedding mapping. It projects word embeddings from a target language to a source language, so that a well-trained source-language neural network RE model can be directly applied to the target language. Experiment results show that the proposed approach achieves very good performance for a number of target languages on both in-house and open datasets, using a small bilingual dictionary with only 1K word pairs.
%R 10.18653/v1/D19-1038
%U https://aclanthology.org/D19-1038
%U https://doi.org/10.18653/v1/D19-1038
%P 399-409

Markdown (Informal)

[Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping](https://aclanthology.org/D19-1038) (Ni & Florian, EMNLP-IJCNLP 2019)

• Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (Ni & Florian, EMNLP-IJCNLP 2019)

ACL

• Jian Ni and Radu Florian. 2019. Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 399–409, Hong Kong, China. Association for Computational Linguistics.