Grammar-based concept alignment for domain-specific Machine Translation

Paper i proceeding, 2021

Grammar-based domain-specific MT systems are a common use case for CNLs. High-quality translation lexica are a crucial part of such systems, but involve time consuming work and significant linguistic knowledge. With parallel example sentences available, statistical alignment tools can help automate part of the process, but they are not suitable for small datasets and do not always perform well with complex multiword expressions. In addition, the correspondences between word forms obtained in this way cannot be used directly. Addressing these problems, we propose a grammar-based approach to this task and put it to test in a simple translation pipeline.