Meemi: A Simple Method for Post-processing Cross-lingual Word Embeddings
Table 2: Cross-lingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation. Languages codes: English-EN, Spanish-ES, Italian-IT, German-DE, and Farsi-FA.
['[BOLD] Model', '[BOLD] EN-ES  [ITALIC] r', '[BOLD] EN-ES  [ITALIC] ρ', '[BOLD] EN-IT  [ITALIC] r', '[BOLD] EN-IT  [ITALIC] ρ', '[BOLD] EN-DE  [ITALIC] r', '[BOLD] EN-DE  [ITALIC] ρ', '[BOLD] EN-FA  [ITALIC] r', '[BOLD] EN-FA  [ITALIC] ρ']
Except in a few cases for the VecMapmultistep model, our Meemi transformation proves superior to the base models, and to all their unsupervised variants. In Farsi, which is the most distant language with a different alphabet, the results are lower overall, but in this case our Meemi transformation proves essential, outperforming the best VecMap model by almost four percentage points (from 36.5% to 40.4%). Similarly as in the bilingual dictionary induction task, the weighted version of Meemi proves robust only on English-Farsi, which suggests that this weighting scheme is most useful for distant languages, as in this case the Farsi monolingual space (which is learned from a smaller corpus and hence, as we will see in the next section, has a lower quality) gets closer to the English monolingual space. As far as the multilingual model is concerned, it proves beneficial in all cases with respect to the orthogonal version of VecMap, as well as compared to the bilingual variant of Meemi.