Crosslingual Document Embedding as Reduced-Rank Ridge Regression
Table 3. Precision at k on sentence retrieval.222 (“AdvRefine (cosine)” not reported 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']
For the evaluation, we first map all query and candidate sentences to the embedding space using the pairwise English/Italian model trained on Wikipedia (Sec. Then, given a query sentence, we aim at retrieving its correct translation in the target language. Comparing to prior methods, we perform consistently better than Dinu et al. (Conneau et al., As our embedding is trained at a vastly different level of text granularity (documents rather than sentences) and on a vastly different type of corpus (Wikipedia rather than parliament debates), these results demonstrate that Cr5 generalizes well across text lengths and corpora.