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.
@article{arxiv.1802.04335,
title = {Neural Program Search: Solving Programming Tasks from Description and Examples},
author = {Illia Polosukhin and Alexander Skidanov},
journal= {arXiv preprint arXiv:1802.04335},
year = {2018}
}