@InProceedings{asghar-EtAl:2017:starSEM,
  author    = {Asghar, Nabiha  and  Poupart, Pascal  and  Jiang, Xin  and  Li, Hang},
  title     = {Deep Active Learning for Dialogue Generation},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {78--83},
  abstract  = {We propose an online, end-to-end, neural generative conversational model for
	open-domain dialogue. It is trained using a unique combination of offline
	two-phase supervised learning and online human-in-the-loop active learning.
	While most existing research proposes offline supervision or hand-crafted
	reward functions for online reinforcement, we devise a novel interactive
	learning mechanism based on hamming-diverse beam search for response generation
	and one-character user-feedback at each step. Experiments show that our model
	inherently promotes the generation of semantically relevant and interesting
	responses, and can be used to train agents with customized personas, moods and
	conversational styles.},
  url       = {http://www.aclweb.org/anthology/S17-1008}
}