Are All Good Word Vector Spaces Isomorphic?
Table 2: BLI scores (MRR) when mapping from a fully trained en embedding space to one trained on full Wikipedia corpora, random samples, and topic-adjusted comparable samples of the same size with and without lemmatisation for Spanish (es) and Basque (eu), Quechua (qu), and Galician (gl), respectively (Top table); Bengali (bn) and Tamil (ta), and bn and Urdu (ur) (Bottom table).
['[EMPTY]', 'Tamil (ta) en-bn', 'Tamil (ta) en-ta', 'Tamil (ta) bn/ta gap', 'Urdu (ur) en-bn', 'Urdu (ur) en-ur', 'Urdu (ur) bn/ur gap']
We observe that inequality in training resources accounts for a large part of the performance gap. Controlling for topical skew and morphology reduces the gap further and results in nearly identical performance for Spanish compared to Quechua and Galician, respectively. For Galician, lemmatisation slightly widens the gap, likely due to a weaker lemmatiser. For Basque, the remaining gap may be explained by the remaining typological differences between the two languages. ta, as well as between en–bn and en–ur. This again hints that other factors besides inherent language dissimilarity are at play and contribute to reduced isomorphism between embedding spaces.