Compiling Rewrite Rules to Finite-State Transducers with the Worsening Trick
Researchers have developed a new method for compiling string rewrite rules into finite-state transducers (FSTs), a crucial step for natural language processing tasks like phonological and morphological analysis. This novel approach utilizes the "worsening trick" to generate and then filter rewrite candidates, offering a more compact and uniform compilation scheme. The method has been implemented in PyFoma and validated against existing systems, demonstrating its effectiveness and extensibility for various rewriting modalities. AI
IMPACT This research offers a more efficient method for string rewriting, potentially improving NLP model performance on tasks involving morphology and phonology.