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.

本文提出了基于双语词向量映射的方法，将一个好的源语言神经网络关系抽取模型直接应用于目标语言，从而实现了跨语言关系抽取模型的转移。实验证明该方法在多种目标语言中表现出较好的性能。

基于双语词嵌入映射的神经跨语言关系抽取

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

Most of the existing methods for bilingual word embedding only consider
shallow context or simple co-occurrence information. In this paper, we propose
a latent bilingual sense unit (Bilingual Sense Clique, BSC), which is derived
from a maximum complete sub-graph of pointwise mutual information based graph
over bilingual corpus. In this way, we treat source and target words equally
and a separated bilingual projection processing that have to be used in most
existing works is not necessary any more. Several dimension reduction methods
are evaluated to summarize the BSC-word relationship. The proposed method is
evaluated on bilingual lexicon translation tasks and empirical results show
that bilingual sense embedding methods outperform existing bilingual word
embedding methods.

本研究提出了一种基于点互信息图的最大完整子图（Bilingual Sense Clique, BSC）从而将源语言和目标语言的词汇平等地处理，不需要分别使用双语投影处理，采用几种降维方法对 BSC - 词汇关系进行总结。经实验证明，该方法优于现有的双语词嵌入方法在双语词汇翻译任务上的效果。