Learning to Model Structures and Execution for Program Synthesis

Deep neural networks have achieved remarkable success in natural language processing and code modeling, especially with the advancement of pre-training techniques. In this talk, I will discuss my neural program synthesis research, with a focus of developing program synthesizers that learn to infer the user intents from different specification formats, and can be deployed in production.

First, I will discuss my SpreadsheetCoder work, where we aim to predict spreadsheet formulas from the user-written tabular data. The SpreadsheetCoder model was integrated into Google Sheets, and is available to all Google users. In the second part of my talk, I will discuss my work on execution-guided techniques for program synthesis from input-output examples. We show that utilizing and modeling partial program execution significantly improves the program synthesis performance, especially for programming languages that include control flow constructs such as conditionals and loops.