Learning Multilingual Word Embeddings inLatent Metric Space: A Geometric Approach
Table 2: Precision@1 for BLI on the VecMap dataset. The results of MSF-ISF, SL-unsup, CCA-NN (Faruqui and Dyer, 2014), and Adv-Refine are reported by Artetxe et al. (2018b). CCA-NN employs nearest neighbor retrieval procedure. The results of GPA are reported by Kementchedjhieva et al. (2018).
['[BOLD] Method', '[BOLD] en-it', '[BOLD] en-de', '[BOLD] en-fi', '[BOLD] en-es', '[BOLD] avg.']
Results on VecMap dataset: We observe that GeoMM obtains the best performance in each language pair, surpassing state-of-the-art results reported on this dataset.