Language To Code: Learning Semantic Parsers For If-This-Then-That Recipes - Quirk, Chris; Mooney, Raymond; Galley, Michel (Proc. of ACL, 2015)
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.