  A popular natural language processing task decades ago, word alignment has
been dominated until recently by GIZA++, a statistical method based on the
30-year-old IBM models. Though recent years have finally seen Giza++
performance bested, the new methods primarily rely on large machine translation
models, massively multilingual language models, or supervision from Giza++
alignments itself. We introduce Embedding-Enhanced Giza++, and outperform
Giza++ without any of the aforementioned factors. Taking advantage of
monolingual embedding space geometry of the source and target language only, we
exceed Giza++'s performance in every tested scenario for three languages. In
the lowest-resource scenario of only 500 lines of bitext, we improve
performance over Giza++ by 10.9 AER. Our method scales monotonically
outperforming Giza++ for all tested scenarios between 500 and 1.9 million lines
of bitext. Our code will be made publicly available.
