Crosslingual Document Embedding as Reduced-Rank Ridge Regression
Table 4. Precision at k on word retrieval.4.3 (“Procrustes (cosine)” and “AdvRefine (cosine)” omitted by authors (Conneau et al., 2017).)
['[EMPTY]', 'Query in Italian P@1', 'Query in Italian P@5', 'Query in Italian P@10', 'Query in English P@1', 'Query in English P@5', 'Query in English P@10']
Interestingly, however, the increase from P@1 to P@5 and P@10 is less pronounced here than for sentence retrieval, such that several methods achieve higher performance than Cr5 in terms of P@5 and P@10. Nonetheless, given that the competing methods were trained specifically for embedding words, whereas ours was trained for embedding documents, we are pleased with this competitive performance at the word level.