mlsanityData to 14 Oct 2026

SPARQLing Database Queries from Intermediate Question Decompositions

arXiv 2109.06162 ↗First posted 13 Sep 2021cs.CLcited by 7

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Abstract (arXiv)

To translate natural language questions into executable database queries, most approaches rely on a fully annotated training set. Annotating a large dataset with queries is difficult as it requires query-language expertise. We reduce this burden using grounded in databases intermediate question representations. These representations are simpler to collect and were originally crowdsourced within the Break dataset (Wolfson et al., 2020). Our pipeline consists of two parts: a neural semantic parser that converts natural language questions into the intermediate representations and a non-trainable transpiler to the SPARQL query language (a standard language for accessing knowledge graphs and semantic web). We chose SPARQL because its queries are structurally closer to our intermediate representations (compared to SQL). We observe that the execution accuracy of queries constructed by our model on the challenging Spider dataset is comparable with the state-of-the-art text-to-SQL methods trained with annotated SQL queries. Our code and data are publicly available (see https://github.com/yandex-research/sparqling-queries).

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 34 of its 54 references that are in the map. First posted on arXiv 13 Sep 2021.

Builds on

34 of 54 references.

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22 more
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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 0; 2025: 1.

7 papers.

  1. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  2. 2021Weakly Supervised Text-to-SQL Parsing through Question Decompositioncited by 15
  3. 2023Semantic Decomposition of Question and SQL for Text-to-SQL Parsingcited by 14
  4. 2025Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsingcited by 13
  5. 2021Learning to Transpile AMR into SPARQLcited by 4
  6. 2023NeuroQL: A Neuro-Symbolic Language and Dataset for Inter-Subjective Reasoningcited by 1
  7. 2022Searching for Better Database Queries in the Outputs of Semantic Parserscited by 0