@InProceedings{goyal-dymetman-gaussier:2016:COLING,
  author    = {Goyal, Raghav  and  Dymetman, Marc  and  Gaussier, Eric},
  title     = {Natural Language Generation through Character-based RNNs with Finite-state Prior Knowledge},
  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     = {1083--1092},
  abstract  = {Recently Wen et al. (2015) have proposed a Recurrent Neural Network (RNN)
	approach to the generation of utterances from dialog acts, and shown that
	although their model requires less effort to develop than a rule-based system,
	it is able to improve certain aspects of the utterances, in particular their
	naturalness. However their system employs generation at the word-level, which
	requires one to pre-process the data by substituting named entities with
	placeholders. This pre-processing prevents the model from handling some
	contextual effects and from managing multiple occurrences of the same
	attribute.
	Our approach uses a character-level model, which unlike the word-level model
	makes it possible to learn to ``copy" information from the dialog act to the
	target without having to pre-process the input. In order to avoid generating
	non-words and inventing information not present in the input, we propose a
	method for incorporating prior knowledge into the RNN in the form of a weighted
	finite-state automaton over character sequences. Automatic and human
	evaluations show improved performance over baselines on several evaluation
	criteria.},
  url       = {http://aclweb.org/anthology/C16-1103}
}