  Bilingual lexicons map words in one language to their translations in
another, and are typically induced by learning linear projections to align
monolingual word embedding spaces. In this paper, we show it is possible to
produce much higher quality lexicons with methods that combine (1) unsupervised
bitext mining and (2) unsupervised word alignment. Directly applying a pipeline
that uses recent algorithms for both subproblems significantly improves induced
lexicon quality and further gains are possible by learning to filter the
resulting lexical entries, with both unsupervised and semi-supervised schemes.
Our final model outperforms the state of the art on the BUCC 2020 shared task
by 14 $F_1$ points averaged over 12 language pairs, while also providing a more
interpretable approach that allows for rich reasoning of word meaning in
context.
