TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking

Michael Heck, Carel van Niekerk, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Marco Moresi, Milica Gasic


Abstract
Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user’s goal during the course of an interaction. Multi-domain and open-vocabulary settings complicate the task considerably and demand scalable solutions. In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values. Our model has no need to maintain a list of candidate values. Instead, all values are extracted from the dialog context on-the-fly. A slot is filled by one of three copy mechanisms: (1) Span prediction may extract values directly from the user input; (2) a value may be copied from a system inform memory that keeps track of the system’s inform operations (3) a value may be copied over from a different slot that is already contained in the dialog state to resolve coreferences within and across domains. Our approach combines the advantages of span-based slot filling methods with memory methods to avoid the use of value picklists altogether. We argue that our strategy simplifies the DST task while at the same time achieving state of the art performance on various popular evaluation sets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55%.
Anthology ID:
2020.sigdial-1.4
Volume:
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
July
Year:
2020
Address:
1st virtual meeting
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–44
Language:
URL:
https://aclanthology.org/2020.sigdial-1.4
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.sigdial-1.4.pdf
Video:
 https://youtube.com/watch?v=qWLnp4tPbPM