Spoken Dialogue for Information Navigation

Alexandros Papangelis, Panagiotis Papadakos, Yannis Stylianou, Yannis Tzitzikas


Abstract
Aiming to expand the current research paradigm for training conversational AI agents that can address real-world challenges, we take a step away from traditional slot-filling goal-oriented spoken dialogue systems (SDS) and model the dialogue in a way that allows users to be more expressive in describing their needs. The goal is to help users make informed decisions rather than being fed matching items. To this end, we describe the Linked-Data SDS (LD-SDS), a system that exploits semantic knowledge bases that connect to linked data, and supports complex constraints and preferences. We describe the required changes in language understanding and state tracking, and the need for mined features, and we report the promising results (in terms of semantic errors, effort, etc) of a preliminary evaluation after training two statistical dialogue managers in various conditions.
Anthology ID:
W18-5025
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
SIGDIAL | WS
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
229–234
Language:
URL:
https://aclanthology.org/W18-5025
DOI:
10.18653/v1/W18-5025
Bibkey:
Cite (ACL):
Alexandros Papangelis, Panagiotis Papadakos, Yannis Stylianou, and Yannis Tzitzikas. 2018. Spoken Dialogue for Information Navigation. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 229–234, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Spoken Dialogue for Information Navigation (Papangelis et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5025.pdf