  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.
