Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Table 9: Comparison between a nearest neighbor (NN) criterion and CSLS.
['Method', 'en-es', 'es-en', 'en-fr', 'fr-en', 'en-de', 'de-en', 'en-ru', 'ru-en', 'en-zh', 'zh-en', 'avg.']
Every approach uses the Wikipedia fastText vectors and supervision comes in the form of a lexicon composed of 5k words and their translations. Regardless of the relaxation, RCSLS outperforms the state of the art by, on average, 3 to 4% in accuracy. This shows the importance of using the same criterion during training and inference. Note that the refinement step (“refine”) also uses CSLS to finetune the alignments but leads to a marginal gain for supervised methods.