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        <title>Distilling weighted finite automata from arbitrary probabilistic models</title>
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    <abstract>Weighted finite automata (WFA) are often used to represent probabilistic models, such as n-gram language models, since they are efficient for recognition tasks in time and space. The probabilistic source to be represented as a WFA, however, may come in many forms. Given a generic probabilistic model over sequences, we propose an algorithm to approximate it as a weighted finite automaton such that the Kullback-Leibler divergence between the source model and the WFA target model is minimized. The proposed algorithm involves a counting step and a difference of convex optimization, both of which can be performed efficiently. We demonstrate the usefulness of our approach on some tasks including distilling n-gram models from neural models.</abstract>
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%0 Conference Proceedings
%T Distilling weighted finite automata from arbitrary probabilistic models
%A Suresh, Ananda Theertha
%A Roark, Brian
%A Riley, Michael
%A Schogol, Vlad
%Y Vogler, Heiko
%Y Maletti, Andreas
%S Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing
%D 2019
%8 September
%I Association for Computational Linguistics
%C Dresden, Germany
%F suresh-etal-2019-distilling
%X Weighted finite automata (WFA) are often used to represent probabilistic models, such as n-gram language models, since they are efficient for recognition tasks in time and space. The probabilistic source to be represented as a WFA, however, may come in many forms. Given a generic probabilistic model over sequences, we propose an algorithm to approximate it as a weighted finite automaton such that the Kullback-Leibler divergence between the source model and the WFA target model is minimized. The proposed algorithm involves a counting step and a difference of convex optimization, both of which can be performed efficiently. We demonstrate the usefulness of our approach on some tasks including distilling n-gram models from neural models.
%R 10.18653/v1/W19-3112
%U https://aclanthology.org/W19-3112
%U https://doi.org/10.18653/v1/W19-3112
%P 87-97

Markdown (Informal)

[Distilling weighted finite automata from arbitrary probabilistic models](https://aclanthology.org/W19-3112) (Suresh et al., FSMNLP 2019)

• Distilling weighted finite automata from arbitrary probabilistic models (Suresh et al., FSMNLP 2019)

ACL

• Ananda Theertha Suresh, Brian Roark, Michael Riley, and Vlad Schogol. 2019. Distilling weighted finite automata from arbitrary probabilistic models. In Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing, pages 87–97, Dresden, Germany. Association for Computational Linguistics.