Searching for Better Database Queries in the Outputs of Semantic Parsers

Anton Osokin, Irina Saparina, Ramil Yarullin

Abstract
The task of generating a database query from a question in natural language suffers from ambiguity and insufficiently precise description of the goal. The problem is amplified when the system needs to generalize to databases unseen at training. In this paper, we consider the case when, at the test time, the system has access to an external criterion that evaluates the generated queries. The criterion can vary from checking that a query executes without errors to verifying the query on a set of tests. In this setting, we augment neural autoregressive models with a search algorithm that looks for a query satisfying the criterion. We apply our approach to the state-of-the-art semantic parsers and report that it allows us to find many queries passing all the tests on different datasets.
Anthology ID:
2023.findings-eacl.168
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2243–2256
Language:
URL:
https://aclanthology.org/2023.findings-eacl.168
DOI:
10.18653/v1/2023.findings-eacl.168
Bibkey:
Cite (ACL):
Anton Osokin, Irina Saparina, and Ramil Yarullin. 2023. Searching for Better Database Queries in the Outputs of Semantic Parsers. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2243–2256, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Searching for Better Database Queries in the Outputs of Semantic Parsers (Osokin et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2023.findings-eacl.168.pdf
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