@InProceedings{eric-EtAl:2017:W17-55,
  author    = {Eric, Mihail  and  Krishnan, Lakshmi  and  Charette, Francois  and  Manning, Christopher D.},
  title     = {Key-Value Retrieval Networks for Task-Oriented Dialogue},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {37--49},
  abstract  = {Neural task-oriented dialogue systems often struggle to smoothly interface with
	a knowledge base. In this work, we seek to address this problem by proposing a
	new neural dialogue agent that is able to effectively sustain grounded,
	multi-domain discourse through a novel key-value retrieval mechanism. The model
	is end-to-end differentiable and does not need to explicitly model dialogue
	state or belief trackers. We also release a new dataset of 3,031 dialogues that
	are grounded through underlying knowledge bases and span three distinct tasks
	in the in-car personal assistant space: calendar scheduling, weather
	information retrieval, and point-of-interest navigation. Our architecture is
	simultaneously trained on data from all domains and significantly outperforms a
	competitive rule-based system and other existing neural dialogue architectures
	on the provided domains according to both automatic and human evaluation
	metrics.},
  url       = {http://aclweb.org/anthology/W17-5506}
}