  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.
