  We present word2word, a publicly available dataset and an open-source Python
package for cross-lingual word translations extracted from sentence-level
parallel corpora. Our dataset provides top-k word translations in 3,564
(directed) language pairs across 62 languages in OpenSubtitles2018 (Lison et
al., 2018). To obtain this dataset, we use a count-based bilingual lexicon
extraction model based on the observation that not only source and target words
but also source words themselves can be highly correlated. We illustrate that
the resulting bilingual lexicons have high coverage and attain competitive
translation quality for several language pairs. We wrap our dataset and model
in an easy-to-use Python library, which supports downloading and retrieving
top-k word translations in any of the supported language pairs as well as
computing top-k word translations for custom parallel corpora.
