@InProceedings{luan-EtAl:2017:I17-1,
  author    = {Luan, Yi  and  Brockett, Chris  and  Dolan, Bill  and  Gao, Jianfeng  and  Galley, Michel},
  title     = {Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {605--614},
  abstract  = {Building a persona-based conversation agent is challenging owing to the lack of
	large amounts of speaker-specific conversation data for model training. This
	paper addresses the problem by proposing a multi-task learning approach to
	training neural conversation models that leverages both conversation data
	across speakers and other types of data pertaining to the speaker and speaker
	roles to be modeled. Experiments show that our approach leads to significant
	improvements over baseline model quality, generating responses that capture
	more precisely speakers’ traits and speaking styles. The model offers the
	benefits of being algorithmically simple and easy to implement, and not relying
	on large quantities of data representing specific individual speakers.},
  url       = {http://www.aclweb.org/anthology/I17-1061}
}