Type-driven Neural Programming by Example

TL;DR

This paper introduces a neural program synthesis method that incorporates programming types to improve generalization in programming by example tasks, demonstrating the benefits of type information in neural models.

Contribution

It proposes the Typed Neuro-Symbolic Program Synthesis (TNSPS) approach, integrating type information into neural program synthesis for the first time in this context.

Findings

Type information improves neural synthesis generalization.

TNSPS outperforms baseline models on limited datasets.

Public code release supports reproducibility.

Abstract

In this thesis we look into programming by example (PBE), which is about finding a program mapping given inputs to given outputs. PBE has traditionally seen a split between formal versus neural approaches, where formal approaches typically involve deductive techniques such as SAT solvers and types, while the neural approaches involve training on sample input-outputs with their corresponding program, typically using sequence-based machine learning techniques such as LSTMs [41]. As a result of this split, programming types had yet to be used in neural program synthesis techniques. We propose a way to incorporate programming types into a neural program synthesis approach for PBE. We introduce the Typed Neuro-Symbolic Program Synthesis (TNSPS) method based on this idea, and test it in the functional programming context to empirically verify type information may help improve generalization in neural synthesizers on limited-size datasets. Our TNSPS model builds upon the existing Neuro-Symbolic Program Synthesis (NSPS), a tree-based neural synthesizer combining info from input-output examples plus the current program, by further exposing information on types of those input-output examples, of the grammar production rules, as well as of the hole that we wish to expand in the program. We further explain how we generated a dataset within our domain, which uses a limited subset of Haskell as the synthesis language. Finally we discuss several topics of interest that may help take these ideas further. For reproducibility, we release our code publicly.