  We introduce a novel discriminative latent variable model for bilingual
lexicon induction. Our model combines the bipartite matching dictionary prior
of Haghighi et al. (2008) with a representation-based approach (Artetxe et al.,
2017). To train the model, we derive an efficient Viterbi EM algorithm. We
provide empirical results on six language pairs under two metrics and show that
the prior improves the induced bilingual lexicons. We also demonstrate how
previous work may be viewed as a similarly fashioned latent-variable model,
albeit with a different prior.
