Using natural language to write programs is a touchstone problem for computational linguistics. We present an approach that learns to map natural-language descriptions of simple "if-then" rules to executable code. By training and testing on a large corpus of naturally-occurring programs (called "recipes") and their natural language descriptions, we demonstrate the ability to effectively map language to code. We compare a number of semantic parsing approaches on the highly noisy training data collected from ordinary users, and find that loosely synchronous systems perform best.
REF created models to map natural language descriptions to executable code using productions from the formal language.
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes
ACL
Computer Science