Neural Program Search: Solving Programming Tasks from Description and ExamplesDownload PDF

12 Feb 2018 (modified: 15 Feb 2018)ICLR 2018 Workshop SubmissionReaders: Everyone
Keywords: Deep learning, Structured Prediction, Natural Language Processing, Neural Program Synthesis
TL;DR: Program synthesis from natural language description and input / output examples via Tree-Beam Search over Seq2Tree model
Abstract: We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input/output examples. The algorithm combines methods from Deep Learning and Program Synthesis fields by designing rich domain-specific language (DSL) and defining efficient search algorithm guided by a Seq2Tree model on it. To evaluate the quality of the approach we also present a semi-synthetic dataset of descriptions with test examples and corresponding programs. We show that our algorithm significantly outperforms a sequence-to-sequence model with attention baseline.
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