Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Table 8: Comparison with publicly available aligned vectors, averaged over 28 language pairs. All use supervision. Alignements are learned either on the “Original” or “Full” MUSE training. We report performance with the NN and CSLS criterion on either the full MUSE test set or without the exact string matches. BP uses a different training set with 5k words.
['[EMPTY]', 'BP', 'Original MUSE', 'Original RCSLS', 'Full Proc.', 'Full RCSLS']
RCSLS improves the performance by +3.5% over MUSE vectors when trained with the same lexicon (Original). Training RSCSL on the full training lexicon (Full) brings an additional improvement of +2.9% on average with a CSLS criterion, and +6.1% with a NN criterion. For reference, the performance of Procrustes only improves by +1.4% with CSLS and even degrades with a NN criterion. RCSLS benefits more from additional supervision than Procrustes. Finally, the gap between RCSLS and the other methods is higher with a NN criterion, suggesting that RCSLS imports some of the properties of CSLS to the dot product between aligned vectors.