BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Table 2: Results for the translation task measured as word translation accuracy (out of 100, higher is better) evaluated on the top-1 and top-5 words as ranked by the method. Cross-lingual embeddings are induced and distance in the embedded space are used to select word translation pairs. +x indicates improvement in absolute precision over the previous state-of-the-art on this task (Mikolov et al., 2013a).
['[BOLD] Method', '[BOLD] En→Sp P@1', '[BOLD] Sp→En P@1', '[BOLD] En→Sp P@5', '[BOLD] Sp→En P@5']
We induced 40-dimensional embeddings using the English and Spanish Wikipedias and Europarl as parallel data. Our model improves on both the baselines and on Mikolov et al. ’s method on both tasks and gives a noticeable improvement in accuracy for the P@1 prediction. For the English to Spanish translation, we improve absolute word translation accuracy by 6 percent. For the Spanish to English task, we improve absolute word translation accuracy by 9 percent. This indicates that our model is able to learn fine-grained translation equivalences from the monolingual data by using only the raw-text, sentence-aligned parallel data, despite the lack of word-level alignments or training dictionaries.