Multilingual Factor Analysis
Table 5: Sentence translation precisions @1, @5, @10 on 2,000 English-Italian pairs samples from a set of 200k sentences from Europarl koehn2005europarl on Dinu embeddings. AdvR is copied from Adversarial - Refined in conneau2017word. Rows with ✓ copied from smith2017offline.
['[EMPTY]', 'English to Italian @1', 'English to Italian @5', 'English to Italian @10', 'Italian to English @1', 'Italian to English @5', 'Italian to English @10']
Europarl (koehn2005europarl) is a parallel corpus of sentences taken from the proceedings of the European parliament. In this set of experiments, we focus on its English-Italian (en-it) sub-corpus, in order to compare to previous methods. We report results under the framework of conneau2017word. That is, we form sentence embeddings using the average of the tf-idf weighted word embeddings in the bag-of-words representation of the sentence. Performance is averaged over 2,000 randomly chosen source sentence queries and 200k target sentences for each language pair. Note that this is a different set up to the one presented in smith2017offline, in which an unweighted average is used. As we can see, IBFA outperforms all prior methods both using nearest neighbour retrieval, where it has a gain of 20 percent absolute on SVD, as well as using the CSLS retrieval metric.