@InProceedings{wang-EtAl:2017:EMNLP20176,
  author    = {Wang, Di  and  Jojic, Nebojsa  and  Brockett, Chris  and  Nyberg, Eric},
  title     = {Steering Output Style and Topic in Neural Response Generation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2140--2150},
  abstract  = {We propose simple and flexible training and decoding methods for influencing
	output style and topic in neural encoder-decoder based language generation.
	This capability is desirable in a variety of applications, including
	conversational systems, where successful agents need to produce language in a
	specific style and generate responses steered by a human puppeteer or external
	knowledge. We decompose the neural generation process into empirically easier
	sub-problems: a faithfulness model and a decoding method based on
	selective-sampling. We also describe training and sampling algorithms that bias
	the generation process with a specific language style restriction, or a topic
	restriction. Human evaluation results show that our proposed methods are able
	to to restrict style and topic without degrading output quality in
	conversational tasks.},
  url       = {https://www.aclweb.org/anthology/D17-1228}
}