New Compilation Method for Rewrite Rules in Finite-State Transducers
Researchers have developed a new compilation scheme called the "worsening trick" for converting linguistic rewrite rules into finite-state transducers, which are fundamental tools in natural language processing. The method simplifies the complex process of handling overlapping matches and context constraints that traditional approaches like Kaplan-Kay and Karttunen methods require. This advancement makes it easier to implement and extend phonological and morphological rewriting systems in computational linguistics.
A new paper on arXiv presents an improved method for compiling general rewrite rules of the form A → B / L _ R into finite-state transducers (FSTs), which are essential computational structures in natural language processing. The traditional approaches to this problem, developed by Kaplan and Kay or Karttunen, involve intricate transducer compositions with auxiliary markers to handle overlapping matches and context constraints. The proposed "worsening trick" approach generates all legal rewrite candidates and then filters out candidates that are worse than another candidate for the same input, resulting in shorter and more uniform formulas. The method has been implemented as the built-in rewrite compiler in PyFoma and supports multiple contexts, arbitrary transductions, markup, directed rewriting, weights, and parallel rewriting. Validation against the foma system on substantial collections of rewrite grammars and automated regression suites shows that the resulting transducers match exactly with earlier approaches while being easier to extend.
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- arXiv cs.CLCenter
Compiling Rewrite Rules to Finite-State Transducers with the Worsening Trick
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