Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction

Lingbo Mo, Ashley Lewis, Huan Sun, Michael White


Abstract
Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) in one turn. However, because natural language may contain ambiguity and variability, this is a difficult challenge. In this work, we investigate an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework, aiming to increase the transparency of the parsing process and help the user trust the final answer. We construct INSPIRED, a crowdsourced dialogue dataset derived from the ComplexWebQuestions dataset. Our experiments show that this framework has the potential to greatly improve overall parse accuracy. Furthermore, we develop a pipeline for dialogue simulation to evaluate our framework w.r.t. a variety of state-of-the-art KBQA models without further crowdsourcing effort. The results demonstrate that our framework promises to be effective across such models.
Anthology ID:
2022.findings-acl.28
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
322–342
Language:
URL:
https://aclanthology.org/2022.findings-acl.28
DOI:
10.18653/v1/2022.findings-acl.28
Bibkey:
Cite (ACL):
Lingbo Mo, Ashley Lewis, Huan Sun, and Michael White. 2022. Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction. In Findings of the Association for Computational Linguistics: ACL 2022, pages 322–342, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction (Mo et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.28.pdf
Video:
 https://aclanthology.org/2022.findings-acl.28.mp4
Code
 molingbo/inspired
Data
BREAKGEMSPLASH