Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping

Jian Ni, Radu Florian

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.
Anthology ID:
D19-1038
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
399–409
Language:
URL:
https://aclanthology.org/D19-1038
DOI:
10.18653/v1/D19-1038
Bibkey:
Cite (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.
Cite (Informal):
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (Ni & Florian, EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/ml4al-ingestion/D19-1038.pdf
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