From natural language specifications to program input parsers

We present a method for automatically generating input parsers from English specifications of input file formats. We use a Bayesian generative model to cap-ture relevant natural language phenomena and translate the English specification into a specification tree, which is then trans-lated into a C++ input parser. We model the problem as a joint dependency pars-ing and semantic role labeling task. Our method is based on two sources of infor-mation: (1) the correlation between the text and the specification tree and (2) noisy supervision as determined by the success of the generated C++ parser in reading in-put examples. Our results show that our approach achieves 80.0 % F-Score accu-racy compared to an F-Score of 66.7% produced by a state-of-the-art semantic parser on a dataset of input format speci-fications from the ACM International Col-legiate Programming Contest (which were written in English for humans with no in-tention of providing support for automated processing).1