Before revision:  We propose a novel language distance measure, Eigenvalue Divergence (EVD), which quantifies the degree of isomorphism between two monolingual spaces. We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks,  2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
After revision:  We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks, 2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.