Automated construction of bilingual dictionaries using monolingual embedding
spaces is a core challenge in machine translation. The end performance of these
dictionaries relies upon the geometric similarity of individual spaces, i.e.,
their degree of isomorphism. Existing attempts aimed at controlling the
relative isomorphism of different spaces fail to incorporate the impact of
semantically related words in the training objective. To address this, we
propose GRI that combines the distributional training objectives with attentive
graph convolutions to unanimously consider the impact of semantically similar
words required to define/compute the relative isomorphism of multiple spaces.
Experimental evaluation shows that GRI outperforms the existing research by
improving the average P@1 by a relative score of up to 63.6%. We release the
codes for GRI at this https URL

使用单语嵌入空间自动构建双语词典是机器翻译中的核心挑战，现有的尝试未能考虑语义相关词对于多个空间的相对同构性的影响。为解决这一问题，我们提出了 GRI，通过结合分布式训练目标和注意力图卷积来同时考虑语义相似词对于定义和计算多个空间的相对同构性所需的影响。实验评估表明，GRI 通过提高相对得分达到 63.6% 的平均 P@1 值优于现有研究。我们在此链接上发布了 GRI 的代码。