@inproceedings{quirk2015language,
author = {Quirk, Chris and Mooney, Raymond and Galley, Michel},
title = {Language To Code: Learning Semantic Parsers For If-This-Then-That Recipes},
booktitle = {Proc. of ACL},
year = {2015},
month = {August},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/language-code-learning-semantic-parsers-recipes/},
edition = {Proc. of ACL},
}