Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems

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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.

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Vaduguru, S., Sathe, A., Choudhury, M., & Sharma, D. M. (2021). Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems. In SIGMORPHON 2021 - 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, Proceedings of the Workshop (pp. 60–71). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.sigmorphon-1.7

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