@InProceedings{mcmahan-stone:2016:COLING,
  author    = {McMahan, Brian  and  Stone, Matthew},
  title     = {Syntactic realization with data-driven neural tree grammars},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {224--235},
  abstract  = {A key component in surface realization in natural language generation is to
	choose concrete syntactic relationships to express a target meaning. We develop
	a new method for syntactic choice based on learning a stochastic tree grammar
	in a neural architecture. This framework can exploit state-of-the-art methods
	for modeling word sequences and generalizing across vocabulary. We also induce
	embeddings to generalize over elementary tree structures and exploit a tree
	recurrence over the input structure to model long-distance influences between
	NLG choices. We evaluate the models on the task of linearizing unannotated
	dependency trees, documenting the contribution of our modeling techniques to
	improvements in both accuracy and run time.},
  url       = {http://aclweb.org/anthology/C16-1022}
}