Meemi: A Simple Method for Post-processing Cross-lingual Word Embeddings
Table 3: Monolingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation.
['[BOLD] Model', '[BOLD] English  [ITALIC] r', '[BOLD] English  [ITALIC] ρ', '[BOLD] Spanish  [ITALIC] r', '[BOLD] Spanish  [ITALIC] ρ', '[BOLD] Italian  [ITALIC] r', '[BOLD] Italian  [ITALIC] ρ', '[BOLD] German  [ITALIC] r', '[BOLD] German  [ITALIC] ρ', '[BOLD] Farsi  [ITALIC] r', '[BOLD] Farsi  [ITALIC] ρ']
In this task our multilingual model representing seven languages in a single space clearly stands out, obtaining the best overall results for English, Spanish and Italian, and improving over the base VecMaportho model on the rest. With the exception of German, where the multi-step framework of \citeauthorartetxe2018generalizing \citeyearartetxe2018generalizing proves most effective, the plain Meemi transformation improves over the base models, for both VecMap and MUSE.