Multilingual Factor Analysis
Table 6: Precision @1 when aligning English, French and Italian embeddings to a common space. For SVD, this common space is English, while for MBFA it is the latent space.
['Method', 'en-it', 'it-en', 'en-fr', 'fr-en', 'it-fr', 'fr-it']
As we can see, both methods experience a decrease in overall performance when compared to models fitted on just a pair of languages, however MBFA performs better overall. That is, the direct approaches preserve their performance on translation to and from English, but translation from French to Italian decreases significantly. Meanwhile, MBFA suffers a decrease in each pair of languages, however it retains competitive performance to the direct methods on English translation. It is worth noting that as the number of aligned languages v increases, there are O(v) pairs of languages, one of which is English, and O(v2) pairs in which English does not participate. This suggests that MBFA may generalise past three simultaneously aligned languages better than the direct methods.