@inproceedings{pasti-etal-2024-l,
title = "An {L}* Algorithm for Deterministic Weighted Regular Languages",
author = {Pasti, Clemente and
Karag{\"o}z, Talu and
Nowak, Franz and
Svete, Anej and
Boumasmoud, Reda and
Cotterell, Ryan},
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.468",
pages = "8197--8210",
abstract = "Extracting finite state automata (FSAs) fromblack-box models offers a powerful approachto gaining interpretable insights into complexmodel behaviors. To support this pursuit, wepresent a weighted variant of Angluin{'}s (1987)L* algorithm for learning FSAs. We stay faithful to the original formulation, devising a wayto exactly learn deterministic weighted FSAswhose weights support division. Furthermore,we formulate the learning process in a mannerthat highlights the connection with FSA minimization, showing how L* directly learns aminimal automaton for the target language.",
}
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<abstract>Extracting finite state automata (FSAs) fromblack-box models offers a powerful approachto gaining interpretable insights into complexmodel behaviors. To support this pursuit, wepresent a weighted variant of Angluin’s (1987)L* algorithm for learning FSAs. We stay faithful to the original formulation, devising a wayto exactly learn deterministic weighted FSAswhose weights support division. Furthermore,we formulate the learning process in a mannerthat highlights the connection with FSA minimization, showing how L* directly learns aminimal automaton for the target language.</abstract>
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%0 Conference Proceedings
%T An L* Algorithm for Deterministic Weighted Regular Languages
%A Pasti, Clemente
%A Karagöz, Talu
%A Nowak, Franz
%A Svete, Anej
%A Boumasmoud, Reda
%A Cotterell, Ryan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F pasti-etal-2024-l
%X Extracting finite state automata (FSAs) fromblack-box models offers a powerful approachto gaining interpretable insights into complexmodel behaviors. To support this pursuit, wepresent a weighted variant of Angluin’s (1987)L* algorithm for learning FSAs. We stay faithful to the original formulation, devising a wayto exactly learn deterministic weighted FSAswhose weights support division. Furthermore,we formulate the learning process in a mannerthat highlights the connection with FSA minimization, showing how L* directly learns aminimal automaton for the target language.
%U https://aclanthology.org/2024.emnlp-main.468
%P 8197-8210
Markdown (Informal)
[An L* Algorithm for Deterministic Weighted Regular Languages](https://aclanthology.org/2024.emnlp-main.468) (Pasti et al., EMNLP 2024)
ACL
- Clemente Pasti, Talu Karagöz, Franz Nowak, Anej Svete, Reda Boumasmoud, and Ryan Cotterell. 2024. An L* Algorithm for Deterministic Weighted Regular Languages. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8197–8210, Miami, Florida, USA. Association for Computational Linguistics.