Density Matching for Bilingual Word Embedding
Table 2: BLI Precision (@1) for morphologically complex languages. id+Procrustes (R)∗ is the result reported in (Søgaard et al., 2018). 5k+Procrustes (R) uses the training dictionary with 5k unique query words. (’et’ is Estonian, ’fi’ is Finnish, ’el’ is Greek, ’hu’ is Hungarian, ’pl’ is Persian, ’tr’ is Turkish.)
['[EMPTY]', 'en-et', 'et-en', 'en-fi', 'fi-en', 'en-el', 'el-en', 'en-hu', 'hu-en', 'en-pl', 'pl-en', 'en-tr', 'tr-en']
Tab. For each language pair we run experiments in both directions. The baseline methods in Søgaard et al. We run id+Procrustes (R) ourselves and obtain different results from them: except for en-et and en-el, we obtain significantly better results on other language pairs. In addition, we add another strong supervised baseline (5k+Procrustes (R)) with the training dictionary in the MUSE dataset and iterative Procrustes refinement. From Tab.