Bilingual Lexicon Induction through Unsupervised Machine Translation
Table 1: P@1 of proposed system and previous retrieval methods, using the same cross-lingual embeddings.
['[EMPTY]', 'en-es →', 'en-es ←', 'en-fr →', 'en-fr ←', 'en-de →', 'en-de ←', 'en-ru →', 'en-ru ←', 'avg.']
As it can be seen, our method obtains the best results in all language pairs and directions, with an average improvement of 6 points over nearest neighbor and 4 points over CSLS, which is the best performing previous method. These results are very consistent across all translation directions, with an absolute improvement between 2.7 and 6.3 points over CSLS. Interestingly, neither inverted nearest neighbor nor inverted softmax are able to outperform standard nearest neighbor, presumably because our cross-lingual embeddings are less sensitive to hubness thanks to the symmetric re-weighting in VecMap At the same time, CSLS obtains an absolute improvement of 2 points over nearest neighbor, only a third of what our method achieves. This suggests that, while previous retrieval methods have almost exclusively focused on addressing the hubness problem, there is a substantial margin of improvement beyond this phenomenon.