Deep Generative Model for Joint Alignment and Word RepresentationCode available from https://github.com/uva-slpl/embedalign
Table 1: Training data size (in millions).
['Corpus', 'Sentence pairs', 'Tokens']
Training data We train our model on bilingual parallel data. In particular, we use parliament proceedings (Europarl-v7) We employed very minimal preprocessing, namely, tokenisation and lowercasing using scripts from Moses ( To cover the vocabulary of the typical benchmark, we have to use a much larger bilingual collection than Europarl. Results in this section are based on EmbedAlign (with bidirectional variational encoder) trained on the Giga web corpus ( Due to the scale of the experiment, we report on a single run.