Existing algorithms for aligning cross-lingual word vector spaces assume that
vector spaces are approximately isomorphic. As a result, they perform poorly or
fail completely on non-isomorphic spaces. Such non-isomorphism has been
hypothesised to result from typological differences between languages. In this
work, we ask whether non-isomorphism is also crucially a sign of degenerate
word vector spaces. We present a series of experiments across diverse languages
which show that variance in performance across language pairs is not only due
to typological differences, but can mostly be attributed to the size of the
monolingual resources available, and to the properties and duration of
monolingual training (e.g. "under-training").

研究跨语言字向量空间对齐的算法，证明非同构空间不一定是由于语言间的类型差异，而是受到单语资源的大小和单语训练的影响，尤其是训练不充分的影响。

所有良好的词向量空间都同构吗？

Are All Good Word Vector Spaces Isomorphic?

Most recent approaches to bilingual dictionary induction find a linear
alignment between the word vector spaces of two languages. We show that
projecting the two languages onto a third, latent space, rather than directly
onto each other, while equivalent in terms of expressivity, makes it easier to
learn approximate alignments. Our modified approach also allows for supporting
languages to be included in the alignment process, to obtain an even better
performance in low resource settings.

本文提出了一种新方法，即通过将两种语言映射到一个潜在的第三个空间中，而不是直接对其进行线性对齐，从而更容易地学习到近似的对齐，在低资源情况下可以通过支持语言进一步提高性能的双语词典归纳方法。