BinaryAlign: Word Alignment as Binary Sequence Labeling
Gaetan Lopez Latouche, Marc‐André Carbonneau, Benjamin Swanson · 2024
Real world deployments of word alignment are almost certain to cover both high and low resource languages.However, the state-ofthe-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.1