Meemi: A Simple Method for Post-processing Cross-lingual Word Embeddings
Table 1: P@K performance of different cross-lingual embedding models in the bilingual dictionary induction task.
['[BOLD] Model', '[BOLD] English-Spanish  [ITALIC] P@1', '[BOLD] English-Spanish  [ITALIC] P@5', '[BOLD] English-Spanish  [ITALIC] P@10', '[BOLD] English-Italian  [ITALIC] P@1', '[BOLD] English-Italian  [ITALIC] P@5', '[BOLD] English-Italian  [ITALIC] P@10', '[BOLD] English-German  [ITALIC] P@1', '[BOLD] English-German  [ITALIC] P@5', '[BOLD] English-German  [ITALIC] P@10']
Not surprisingly, the impact of Meemi is more apparent when used in combination with the orthogonal base models. On the other hand, using the weighted version of Meemi (i.e., Meemiw) does not seem to be particularly beneficial on this task, with the only exception of English-Farsi. In general, the performance of unsupervised models (i.e., VecMapuns and MUSEuns) is competitive in closely-related languages such as English-Spanish or English-German but they considerably under-perform for distant languages, especially English-Finnish and English-Russian. Finally, the results obtained by the multilingual model that includes all seven languages considered, i.e., Meemi-multi (VecMaportho), improve over the base orthogonal model, but they do not improve over the results of our bilingual model.