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        <title>Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems</title>
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        <namePart type="given">Saujas</namePart>
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        <namePart type="given">Aalok</namePart>
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            <title>Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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            <namePart type="given">Garrett</namePart>
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    <abstract>Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples? We explore this question using program synthesis. We develop a synthesis model to learn phonology rules as programs in a domain-specific language. We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability. In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.</abstract>
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    <identifier type="doi">10.18653/v1/2021.sigmorphon-1.7</identifier>
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        <date>2021-08</date>
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%0 Conference Proceedings
%T Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems
%A Vaduguru, Saujas
%A Sathe, Aalok
%A Choudhury, Monojit
%A Sharma, Dipti
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F vaduguru-etal-2021-sample
%X Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples? We explore this question using program synthesis. We develop a synthesis model to learn phonology rules as programs in a domain-specific language. We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability. In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.
%R 10.18653/v1/2021.sigmorphon-1.7
%U https://aclanthology.org/2021.sigmorphon-1.7
%U https://doi.org/10.18653/v1/2021.sigmorphon-1.7
%P 60-71

Markdown (Informal)

[Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems](https://aclanthology.org/2021.sigmorphon-1.7) (Vaduguru et al., SIGMORPHON 2021)

• Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems (Vaduguru et al., SIGMORPHON 2021)

ACL

• Saujas Vaduguru, Aalok Sathe, Monojit Choudhury, and Dipti Sharma. 2021. Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems. In Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 60–71, Online. Association for Computational Linguistics.