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.
Program synthesis from natural language description and input / output examples via Tree-Beam Search over Seq2Tree model Presents a seq2Tree model to translate a problem statement in natural language to the corresponding functional program in DSL, which has shown an improvement over the seq2seq baseline approach. This paper tackles the problem of doing program synthesis when given a problem description and a small number of input-output examples. The paper introduces a technique for program synthesis involving a restricted grammar of problems that is beam-searched using an attentional encoder-decoder network.