mlsanityData to 14 Oct 2026

Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play

arXiv 2210.12096 ↗First posted 21 Oct 2022cs.CLcited by 9

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Abstract (arXiv)

The task of context-dependent text-to-SQL aims to convert multi-turn user utterances to formal SQL queries. This is a challenging task due to both the scarcity of training data from which to learn complex contextual dependencies and to generalize to unseen databases. In this paper we explore augmenting the training datasets using self-play, which leverages contextual information to synthesize new interactions to adapt the model to new databases. We first design a SQL-to-text model conditioned on a sampled goal query, which represents a user's intent, that then converses with a text-to-SQL semantic parser to generate new interactions. We then filter the synthesized interactions and retrain the models with the augmented data. We find that self-play improves the accuracy of a strong baseline on SParC and CoSQL, two widely used cross-domain text-to-SQL datasets. Our analysis shows that self-play simulates various conversational thematic relations, enhances cross-domain generalization and improves beam-search.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 37 of its 57 references that are in the map. First posted on arXiv 21 Oct 2022.

Builds on

37 of 57 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 1.

9 papers.

  1. 2023SUQL: Conversational Search over Structured and Unstructured Data with Large Language Modelscited by 13
  2. 2024Grounding Data Science Code Generation with Input-Output Specificationscited by 7
  3. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  4. 2024QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQLcited by 5
  5. 2025SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuningcited by 5
  6. 2023Controllable Data Augmentation for Context-Dependent Text-to-SQLcited by 1
  7. 2023Translating Natural Language Queries to SQL Using the T5 Modelcited by 0
  8. 2024Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferencescited by 0
  9. 2026COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Trainingcited by 0