Researchers Develop Method to Automatically Construct Finite-State Transducers from Neural Networks
A team of researchers has developed a novel technique for automatically building finite-state transducers (FSTs) by extracting the hidden state geometry learned by recurrent neural networks. FSTs are compact, efficient models used in tasks like morphological inflection, grapheme-to-phoneme conversion, and historical text normalization, but have traditionally been difficult to construct by hand. The method substantially outperforms classical transducer learning algorithms, achieving accuracy improvements of up to 87 percentage points on held-out test sets.
The paper, accepted to ACL 2026 Findings, introduces a method for inducing unweighted finite-state transducers from recurrent neural networks by leveraging the geometric structure of the network's learned hidden states. Finite-state transducers are widely used in computational linguistics and natural language processing for efficient string-to-string rewriting, but manual construction is labor-intensive and error-prone. The proposed approach automates this process, bridging neural representation learning with classical formal language theory. Evaluations were conducted on real-world datasets spanning morphological inflection, grapheme-to-phoneme prediction, and historical normalization tasks. Results show the induced FSTs are highly accurate and robust across many datasets, outperforming classical transducer induction algorithms by up to 87% accuracy on held-out test sets. The work represents a significant step toward making interpretable, efficient symbolic models more accessible through neural learning.
What's missing
The paper focuses on unweighted FSTs; it is unclear how the method would extend to weighted FSTs, which are more expressive and commonly used in production NLP pipelines. The abstract does not detail failure cases or datasets where the method underperforms, nor does it discuss computational cost of the induction process relative to simply deploying the underlying RNN. Scalability to larger or more morphologically complex languages remains an open question.
What different sources said
- arXiv cs.CLCenter
Neural Induction of Finite-State Transducers
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