  Real world deployments of word alignment are almost certain to cover both
high and low resource languages. However, the state-of-the-art for this task
recommends a different model class depending on the availability of gold
alignment training data for a particular language pair. We propose BinaryAlign,
a novel word alignment technique based on binary sequence labeling that
outperforms existing approaches in both scenarios, offering a unifying approach
to the task. Additionally, we vary the specific choice of multilingual
foundation model, perform stratified error analysis over alignment error type,
and explore the performance of BinaryAlign on non-English language pairs. We
make our source code publicly available.
