Meemi: A Simple Method for Post-processing Cross-lingual Word Embeddings
Table 5: Accuracy on the XNLI task using different cross-lingual embeddings as features.
['[BOLD] Model', '[BOLD] EN-ES', '[BOLD] EN-DE']
The main conclusion in light of these results is the remarkable performance of the unsupervised VecMap model and, most notably, multilingual Meemi for both Spanish and German, clearly outperforming the orthogonal bilingual mapping baseline. Our results are encouraging for two reasons. First, they suggest that, at least for this task, collapsing several languages into a unified vector space is better than performing pairwise alignments. And second, the inherent benefit of having one single model accounting for an arbitrary number of languages.