Neural Program Search: Solving Data Processing Tasks from Description and Examples

Illia Polosukhin, Alexander Skidanov

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 sequence-to-sequence model with attention baseline.

TL;DR: Program synthesis from natural language description and input / output examples via Tree-Beam Search over Seq2Tree model

Keywords: Deep learning, Structured Prediction, Natural Language Processing, Neural Program Synthesis