Researchers Develop Method to Automatically Construct Finite-State Transducers from Neural Networks
Researchers have proposed a novel method for automatically constructing unweighted Finite-State Transducers (FSTs) by extracting patterns learned by recurrent neural networks, eliminating the need for manual construction. FSTs are computational models used for string-to-string rewriting tasks in applications like morphological inflection and speech processing. The approach achieved up to 87% accuracy improvements over classical transducer learning algorithms on real-world datasets, potentially streamlining the development of efficient language processing systems.
A research paper accepted to ACL 2026 Findings describes a novel approach to automatically constructing unweighted Finite-State Transducers by leveraging the hidden state geometry learned by recurrent neural networks. Finite-State Transducers are computational models that efficiently perform string-to-string rewriting tasks, but have traditionally required manual construction, which is labor-intensive and error-prone. The researchers evaluated their method on three real-world applications: morphological inflection, grapheme-to-phoneme prediction, and historical text normalization. The constructed FSTs demonstrated high accuracy and robustness across many datasets, substantially outperforming classical transducer learning algorithms by margins up to 87% on held-out test sets. This work bridges neural and symbolic approaches to language processing, potentially enabling more efficient deployment of language models in production systems.
What different sources said
- arXiv cs.CLCenter
Neural Induction of Finite-State Transducers
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