Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2021
Abstract
Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-theart methods in terms of joint belief accuracy for MultiWOZ 2.1, a large-scale human–human dialogue dataset across multiple domains.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Publication
Transactions of the Association for Computational Linguistics
Volume
9
First Page
557
Last Page
569
ISSN
2307-387X
Identifier
10.1162/tacl_a_00384
Publisher
Massachusetts Institute of Technology Press
Citation
LIAO, Lizi; LONG, Le Hong; MA, Yunshan; LEI, Wenqiang; and CHUA, Tat-Seng.
Dialogue state tracking with incremental reasoning. (2021). Transactions of the Association for Computational Linguistics. 9, 557-569.
Available at: https://ink.library.smu.edu.sg/sis_research/7577
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1162/tacl_a_00384