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Count-based word alignment methods, such as the IBM models or fast-align,struggle on very small parallel corpora. We therefore present an alternativeapproach based on cross-lingual word embeddings (CLWEs), which are trained onpurely monolingual data. Our main contribution is an unsupervised objective toadapt CLWEs to parallel corpora. In experiments on between 25 and 500sentences, our method outperforms fast-align. We also show that our fine-tuningobjective consistently improves a CLWE-only baseline.

Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective