Discovering User Groups for Natural Language Generation

Nikos Engonopoulos, Christoph Teichmann, Alexander Koller

Abstract
We present a model which predicts how individual users of a dialog system understand and produce utterances based on user groups. In contrast to previous work, these user groups are not specified beforehand, but learned in training. We evaluate on two referring expression (RE) generation tasks; our experiments show that our model can identify user groups and learn how to most effectively talk to them, and can dynamically assign unseen users to the correct groups as they interact with the system.
Anthology ID:
W18-5018
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
171–179
Language:
URL:
https://aclanthology.org/W18-5018
DOI:
10.18653/v1/W18-5018
Bibkey:
Cite (ACL):
Nikos Engonopoulos, Christoph Teichmann, and Alexander Koller. 2018. Discovering User Groups for Natural Language Generation. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 171–179, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Discovering User Groups for Natural Language Generation (Engonopoulos et al., SIGDIAL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/W18-5018.pdf
PDF Search