@inproceedings{ginn-etal-2026-neural,
title = "Neural Induction of Finite-State Transducers",
author = "Ginn, Michael and
Palmer, Alexis and
Hulden, Mans",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1411/",
pages = "28322--28336",
ISBN = "979-8-89176-395-1",
abstract = "Finite-State Transducers (FSTs) are effective models for string-to-string rewriting tasks, often providing the efficiency necessary for high-performance applications, but constructing transducers by hand is difficult. In this work, we propose a novel method for automatically constructing unweighted FSTs following the hidden state geometry learned by a recurrent neural network. We evaluate our methods on real-world datasets for morphological inflection, grapheme-to-phoneme prediction, and historical normalization, showing that the constructed FSTs are highly accurate and robust for many datasets, massively outperforming classical transducer learning algorithms by up to 87{\%} accuracy on held-out test sets."
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<abstract>Finite-State Transducers (FSTs) are effective models for string-to-string rewriting tasks, often providing the efficiency necessary for high-performance applications, but constructing transducers by hand is difficult. In this work, we propose a novel method for automatically constructing unweighted FSTs following the hidden state geometry learned by a recurrent neural network. We evaluate our methods on real-world datasets for morphological inflection, grapheme-to-phoneme prediction, and historical normalization, showing that the constructed FSTs are highly accurate and robust for many datasets, massively outperforming classical transducer learning algorithms by up to 87% accuracy on held-out test sets.</abstract>
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%0 Conference Proceedings
%T Neural Induction of Finite-State Transducers
%A Ginn, Michael
%A Palmer, Alexis
%A Hulden, Mans
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ginn-etal-2026-neural
%X Finite-State Transducers (FSTs) are effective models for string-to-string rewriting tasks, often providing the efficiency necessary for high-performance applications, but constructing transducers by hand is difficult. In this work, we propose a novel method for automatically constructing unweighted FSTs following the hidden state geometry learned by a recurrent neural network. We evaluate our methods on real-world datasets for morphological inflection, grapheme-to-phoneme prediction, and historical normalization, showing that the constructed FSTs are highly accurate and robust for many datasets, massively outperforming classical transducer learning algorithms by up to 87% accuracy on held-out test sets.
%U https://aclanthology.org/2026.findings-acl.1411/
%P 28322-28336
Markdown (Informal)
[Neural Induction of Finite-State Transducers](https://aclanthology.org/2026.findings-acl.1411/) (Ginn et al., Findings 2026)
ACL
- Michael Ginn, Alexis Palmer, and Mans Hulden. 2026. Neural Induction of Finite-State Transducers. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28322–28336, San Diego, California, United States. Association for Computational Linguistics.