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 \u201cif-then\u201d rules to executable code. By training and testing on a large corpus of naturally-occurring programs(called \u201crecipes\u201d) 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 \ufb01nd that loosely synchronous systems perform best.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 \u201cif-then\u201d rules to executable code. By training and testing on a large corpus of naturally-occurring programs(called \u201crecipes\u201d) and their natural language descriptions, we demonstrate the ability to effectively map language 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