@InProceedings{richardson-kuhn:2017:Long,
  author    = {Richardson, Kyle  and  Kuhn, Jonas},
  title     = {Learning Semantic Correspondences in Technical Documentation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1612--1622},
  abstract  = {We consider the problem of translating high-level textual descriptions to
	formal representations in technical documentation as part of an effort to model
	the meaning of such documentation.  We focus specifically on the problem of
	learning translational correspondences between text descriptions and grounded
	representations in the target documentation, such as formal representation of
	functions or code templates.  Our approach exploits the parallel nature of such
	documentation, or the tight coupling between high-level text and the low-level
	representations we aim to learn. Data is collected by mining technical
	documents for such parallel text-representation pairs, which we use to train a
	simple semantic parsing model. We report new baseline results on sixteen novel
	datasets, including the standard library documentation for nine popular
	programming languages across seven natural languages, and a small collection of
	Unix utility manuals.},
  url       = {http://aclweb.org/anthology/P17-1148}
}