Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes

Using natural language to write programs is a touchstone problem for computational linguistics. We present an approach that learns to map natural-language descrip-tions of simple “if-then ” rules to executable code. By training and testing on a large cor-pus of naturally-occurring programs (called “recipes”) and their natural language de-scriptions, we demonstrate the ability to effectively map language to code. We compare a number of semantic parsing ap-proaches on the highly noisy training data collected from ordinary users, and find that loosely synchronous systems perform best.