KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base

Junzhuo Li, Deyi Xiong


Abstract
In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios. (2) Knowledge base information is not well exploited and incorporated into semantic parsing. To mitigate the two issues, we propose a knowledge-aware fuzzy semantic parsing framework (KaFSP). It defines fuzzy comparison operations in the grammar system for uncertain reasoning based on the fuzzy set theory. In order to enhance the interaction between semantic parsing and knowledge base, we incorporate entity triples from the knowledge base into a knowledge-aware entity disambiguation module. Additionally, we propose a multi-label classification framework to not only capture correlations between entity types and relations but also detect knowledge base information relevant to the current utterance. Both enhancements are based on pre-trained language models. Experiments on a large-scale conversational question answering benchmark demonstrate that the proposed KaFSP achieves significant improvements over previous state-of-the-art models, setting new SOTA results on 8 out of 10 question types, gaining improvements of over 10% F1 or accuracy on 3 question types, and improving overall F1 from 83.01% to 85.33%. The source code of KaFSP is available at https://github.com/tjunlp-lab/KaFSP.
Anthology ID:
2022.acl-long.35
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
461–473
Language:
URL:
https://aclanthology.org/2022.acl-long.35
DOI:
10.18653/v1/2022.acl-long.35
Bibkey:
Cite (ACL):
Junzhuo Li and Deyi Xiong. 2022. KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 461–473, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base (Li & Xiong, ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.35.pdf
Software:
 2022.acl-long.35.software.zip
Code
 tjunlp-lab/kafsp
Data
CSQA