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Bilingual lexicons map words in one language to their translations inanother, and are typically induced by learning linear projections to alignmonolingual word embedding spaces. In this paper, we show it is possible toproduce much higher quality lexicons with methods that combine (1) unsupervisedbitext mining and (2) unsupervised word alignment. Directly applying a pipelinethat uses recent algorithms for both subproblems significantly improves inducedlexicon quality and further gains are possible by learning to filter theresulting lexical entries, with both unsupervised and semi-supervised schemes.Our final model outperforms the state of the art on the BUCC 2020 shared taskby 14 $F_1$ points averaged over 12 language pairs, while also providing a moreinterpretable approach that allows for rich reasoning of word meaning incontext. Further analysis of our output and the standard reference lexiconssuggests they are of comparable quality, and new benchmarks may be needed tomeasure further progress on this task.

Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment