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Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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. ... : SIGMORPHON 2021 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2106.06566
https://arxiv.org/abs/2106.06566
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2
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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3
Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance ...
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4
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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5
TaxiNLI: Taking a Ride up the NLU Hill ...
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