This is pretty cool! But I would've liked to see some eval on smaller datasets (or just a graph of BLEU vs training set size), since the main use for something

This is pretty cool! But I would've liked to see some eval on smaller datasets (or just a graph of BLEU vs training set size), since the main use for something like this is where you don't have parallel data, in which case the monolingual resources are likely to be much smaller too. A few years back I was trying different methods to create bilingual dictionary word-pair candidates (to be post-edited by a linguist – checking is faster than writing) for Saami languages, and tried aligning pure word vectors, but the existing corpora were too small to get good vectors, so I gave up that avenue :( Another neat approach is https://www.isi.edu/natural-language/mt/RLILdecipher.pdf https://www.isi.edu/natural-language/mt/RLILdecipher.pdf , specifically designed for low-resource languages that happen to have a higher-resource related language (e.g. Haitian Creole–French). They train a source language character-based "cipher model" (a weighted finite state transducer that turns source characters/character pairs into target characters/character pairs), to maximise probability over a target language language model.