BinaryAlign: Word Alignment as Binary Sequence Labeling
This addresses the need for a consistent word alignment method across varying data availability, which is incremental as it builds on existing techniques but unifies them.
The paper tackles the problem of word alignment for both high and low resource languages by proposing BinaryAlign, a binary sequence labeling technique that outperforms existing approaches in both scenarios, offering a unifying solution.
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.