@inproceedings{eric2017kvret,
 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.},
 address = {Saarbr\"ucken, Germany},
 author = {Eric, Mihail  and Krishnan, Lakshmi and Charette, Francois and  Manning, Christopher D.},
 booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
 link = {http://www.aclweb.org/anthology/W17-36 6},
 month = {August},
 pages = {37--49},
 publisher = {Association for Computational Linguistics},
 title = {Key-Value Retrieval Networks for Task-Oriented Dialogue},
 url = {https://nlp.stanford.edu/pubs/eric2017kvret.pdf},
 year = {2017}
}