@inproceedings{wang-etal-2022-improving-text,
title = "Improving Text-to-{SQL} Semantic Parsing with Fine-grained Query Understanding",
author = "Wang, Jun and
Ng, Patrick and
Li, Alexander Hanbo and
Jiang, Jiarong and
Wang, Zhiguo and
Xiang, Bing and
Nallapati, Ramesh and
Sengupta, Sudipta",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.31",
doi = "10.18653/v1/2022.emnlp-industry.31",
pages = "306--312",
abstract = "Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance. In addition, without lexical-level fine-grained query understanding, linking between query and database can only rely on fuzzy string match which leads to suboptimal performance in real applications. In view of this, in this paper we present a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural semantic parser (NSP). By jointly modeling query and database, NER model analyzes user intents and identifies entities in the query. NEL model links typed entities to schema and cell values in database. Parser model leverages available semantic information and linking results and synthesizes tree-structured SQL queries based on dynamically generated grammar. Experiments on SQUALL, a newly released semantic parsing dataset, show that we can achieve 56.8{\%} execution accuracy on WikiTableQuestions (WTQ) test set, which outperforms the state-of-the-art model by 2.7{\%}.",
}
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<abstract>Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance. In addition, without lexical-level fine-grained query understanding, linking between query and database can only rely on fuzzy string match which leads to suboptimal performance in real applications. In view of this, in this paper we present a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural semantic parser (NSP). By jointly modeling query and database, NER model analyzes user intents and identifies entities in the query. NEL model links typed entities to schema and cell values in database. Parser model leverages available semantic information and linking results and synthesizes tree-structured SQL queries based on dynamically generated grammar. Experiments on SQUALL, a newly released semantic parsing dataset, show that we can achieve 56.8% execution accuracy on WikiTableQuestions (WTQ) test set, which outperforms the state-of-the-art model by 2.7%.</abstract>
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%0 Conference Proceedings
%T Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding
%A Wang, Jun
%A Ng, Patrick
%A Li, Alexander Hanbo
%A Jiang, Jiarong
%A Wang, Zhiguo
%A Xiang, Bing
%A Nallapati, Ramesh
%A Sengupta, Sudipta
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2022-improving-text
%X Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance. In addition, without lexical-level fine-grained query understanding, linking between query and database can only rely on fuzzy string match which leads to suboptimal performance in real applications. In view of this, in this paper we present a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural semantic parser (NSP). By jointly modeling query and database, NER model analyzes user intents and identifies entities in the query. NEL model links typed entities to schema and cell values in database. Parser model leverages available semantic information and linking results and synthesizes tree-structured SQL queries based on dynamically generated grammar. Experiments on SQUALL, a newly released semantic parsing dataset, show that we can achieve 56.8% execution accuracy on WikiTableQuestions (WTQ) test set, which outperforms the state-of-the-art model by 2.7%.
%R 10.18653/v1/2022.emnlp-industry.31
%U https://aclanthology.org/2022.emnlp-industry.31
%U https://doi.org/10.18653/v1/2022.emnlp-industry.31
%P 306-312
Markdown (Informal)
[Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding](https://aclanthology.org/2022.emnlp-industry.31) (Wang et al., EMNLP 2022)
ACL
- Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, and Sudipta Sengupta. 2022. Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 306–312, Abu Dhabi, UAE. Association for Computational Linguistics.