Learning Multilingual Word Embeddings inLatent Metric Space: A Geometric Approach
Table 7: Comparison of GeoMM and GeoMMsemi with RCSLS (Joulin et al., 2018). Precision@1 for BLI is reported. The results of RCSLS are reported in the original paper. The results of language pairs en-it and it-en are on the VecMap dataset, while others are on the MUSE dataset.
['[BOLD] Method', '[BOLD] en-es', '[BOLD] es-en', '[BOLD] en-fr', '[BOLD] fr-en', '[BOLD] en-de', '[BOLD] de-en', '[BOLD] en-ru', '[BOLD] ru-en', '[BOLD] en-zh', '[BOLD] zh-en', '[BOLD] en-it', '[BOLD] it-en', '[BOLD] avg.']
We observe that the overall performance of GeoMMsemi is slightly better than RCSLS. In addition, our supervised approach GeoMM performs slightly worse than RCSLS, though it does not have the advantage of learning from unlabeled data, as is the case for RCSLS and GeoMMsemi. We also notice that GeoMMsemi improves upon GeoMM in almost all language pairs.