mlsanityData to 14 Oct 2026

From Text to CQL: Bridging Natural Language and Corpus Search Engine

arXiv 2402.13740 ↗First posted 21 Feb 2024cs.CLcited by 1

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Abstract (arXiv)

Natural Language Processing (NLP) technologies have revolutionized the way we interact with information systems, with a significant focus on converting natural language queries into formal query languages such as SQL. However, less emphasis has been placed on the Corpus Query Language (CQL), a critical tool for linguistic research and detailed analysis within text corpora. The manual construction of CQL queries is a complex and time-intensive task that requires a great deal of expertise, which presents a notable challenge for both researchers and practitioners. This paper presents the first text-to-CQL task that aims to automate the translation of natural language into CQL. We present a comprehensive framework for this task, including a specifically curated large-scale dataset and methodologies leveraging large language models (LLMs) for effective text-to-CQL task. In addition, we established advanced evaluation metrics to assess the syntactic and semantic accuracy of the generated queries. We created innovative LLM-based conversion approaches and detailed experiments. The results demonstrate the efficacy of our methods and provide insights into the complexities of text-to-CQL task.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 21 Feb 2024.

Builds on

16 of 30 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  5. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  6. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  7. 2020CodeBLEU: a Method for Automatic Evaluation of Code Synthesiscited by 479
  8. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  9. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  10. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 235
  11. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
  12. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 164
4 more
  1. 2021CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generationcited by 81
  2. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  3. 2023Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMapcited by 9
  4. 2023Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparisoncited by 6

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysiscited by 2