Learning Multilingual Word Embeddings inLatent Metric Space: A Geometric Approach
Table 1: Precision@1 for BLI on the MUSE dataset. Some notations: (a) ‘−’ implies the original paper does not report result for the corresponding language pair, (b) ‘f.c.’ implies the original paper reports their algorithm failed to converge, (c) ‘×’ implies that we could not run the authors’ code successfully for the language pairs, and (d) ‘∗’ implies the results of the algorithm are reported in the original paper. The remaining results were obtained with the official implementation from the authors.
['[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] avg.']
Results on MUSE dataset: We observe that our algorithm GeoMM outperforms all the supervised baselines. GeoMM also obtains significant improvements over unsupervised approaches.