arXiv:2606.10059v1 Announce Type: cross Abstract: Finite-state transducers (FSTs) are essential for modeling string rewriting in computational linguistics and natural language processing (NLP), particularly for phonological and morphological rewrite rules. Compiling general rewrite rules of the form $A \to B / L \, \_ \, R$, where $A$, $B$, $L$, and $R$ are arbitrary regular languages, is complex due to overlapping matches and context constraints. Traditional methods, such as those by Kaplan and Kay or Karttunen, rely on intricate transducer compositions with auxiliary markers. This paper pres
This paper proposes a novel method for compiling complex rewrite rules into finite-state transducers, addressing long-standing challenges in computational linguistics and NLP. The 'worsening trick' offers a new approach to handling overlapping matches and context constraints more effectively.
Improved methods for managing string rewriting are foundational for advancing natural language processing, particularly in areas like phonology, morphology, and potentially robust code compilation. This development could lead to more efficient and accurate language models and software tools.
The ability to more effectively compile intricate rewrite rules into FSTs could simplify the development of advanced language processing systems and improve their performance. This could accelerate progress in various text and code transformation applications.
- · NLP researchers
- · Computational linguists
- · AI developers
- · Software engineers
- · Traditional, less efficient FST compilation methods
More accurate and efficient language models and compilers could be developed using this technique.
This could lead to advancements in areas like machine translation, speech recognition, and automated code refactoring.
The underlying principles may find application in other areas requiring complex pattern matching and transformation, across various domains of AI.
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