Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.

该研究提出了一种新的基于高斯混合模型和标准化流的跨语言词嵌入方法，通过学习两种单语嵌入空间的概率密度，实现跨语言词嵌入。实验结果表明该方法在具有不同语言结构、复杂形态和遥远语系的词性能够实现有效的性能提升。

双语词嵌入密度匹配