Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2021
Abstract
Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.
Keywords
Computational linguistics, Graphic methods, Knowledge based systems, Natural language processing systems, Probability distributions
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
ACL-IJCNLP 2021: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Virtual Conference, August 1-6
First Page
3288
Last Page
3297
ISBN
9781954085527
Identifier
10.18653/v1/2021.acl-long.255
Publisher
ACL
City or Country
Online
Citation
LAN, Yunshi and JIANG, Jing.
Modeling transitions of focal entities for conversational knowledge base question answering. (2021). ACL-IJCNLP 2021: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Virtual Conference, August 1-6. 3288-3297.
Available at: https://ink.library.smu.edu.sg/sis_research/6779
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.18653/v1/2021.acl-long.255