BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Table 1: Classification accuracy and training times for the proposed BilBOWA method compared to Klementiev et al. (Klementiev et al., 2012), Bilingual Auto-encoders (Chandar et al., 2014), and the BiCVM model (Hermann & Blunsom, 2013), on an exact replica of the Reuters cross-lingual document classification task. These methods were all used to induce 40-dimensional embeddings using the same training data. Baseline results are from Klementiev.
['[BOLD] Method', '[ITALIC] en →  [ITALIC] de', '[ITALIC] de →  [ITALIC] en', '[BOLD] Training Time (min)']
In order to make all results comparable, results for all methods reported here were obtained using the same embedding dimensionality of 40 and the same training data. We use the first 500K lines of the English-German Europarl data both as monolingual and parallel training data. We use a vocabulary size of 46,678 for English and 47,903 for German. Since our method is motivated as a faster version of the model proposed by Klementiev et al., we note that we significantly improve upon their results, while training in 6 minutes versus the original 10 days (14,400 minutes). This yields a total factor 2,400 speedup.