@InProceedings{rabinovich-stern-klein:2017:Long,
  author    = {Rabinovich, Maxim  and  Stern, Mitchell  and  Klein, Dan},
  title     = {Abstract Syntax Networks for Code Generation and Semantic Parsing},
  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     = {1139--1149},
  abstract  = {Tasks like code generation and semantic parsing require mapping unstructured
	(or partially structured) inputs to well-formed, executable outputs. We
	introduce abstract syntax networks, a modeling framework for these problems.
	The outputs are represented as abstract syntax trees (ASTs) and constructed by
	a decoder with a dynamically-determined modular structure paralleling the
	structure of the output tree. On the benchmark Hearthstone dataset for code
	generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy,
	compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we
	perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with
	no task-specific engineering.},
  url       = {http://aclweb.org/anthology/P17-1105}
}