@InProceedings{eric-manning:2017:EACLshort,
  author    = {Eric, Mihail  and  Manning, Christopher},
  title     = {A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {468--473},
  abstract  = {Task-oriented dialogue focuses on conversational agents that participate in
	dialogues with user goals on domain-specific topics. In contrast to chatbots,
	which simply seek to sustain open-ended meaningful discourse, existing
	task-oriented agents usually explicitly model user intent and belief states.
	This paper examines bypassing such an explicit representation by depending on a
	latent neural embedding of state and learning selective attention to dialogue
	history together with copying to  incorporate relevant prior context. We
	complement recent work by showing the effectiveness of simple
	sequence-to-sequence neural architectures with a copy mechanism. Our model
	outperforms more complex memory-augmented models by 7\% in per-response
	generation and is on par with the current state-of-the-art on DSTC2, a
	real-world task-oriented dialogue dataset.},
  url       = {http://www.aclweb.org/anthology/E17-2075}
}