Skeletons Matter: Dynamic Data Augmentation for Text-to-Query

• URL: http://arxiv.org/abs/2511.18934v1
• Date: Mon, 24 Nov 2025 09:39:03 GMT
• Title: Skeletons Matter: Dynamic Data Augmentation for Text-to-Query
• Authors: Yuchen Ji, Bo Xu, Jie Shi, Jiaqing Liang, Deqing Yang, Yu Mao, Hai Chen, Yanghua Xiao,
• Abstract summary: We formally define the Text-to-Query task paradigm, unifying semantic parsing tasks across various query languages.<n>We identify query skeletons as a shared optimization target of Text-to-Query tasks, and propose a general dynamic data augmentation framework.<n> Experiments on four Text-to-Query benchmarks demonstrate that our method achieves state-of-the-art performance.
• Score: 66.52311036179294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of translating natural language questions into query languages has long been a central focus in semantic parsing. Recent advancements in Large Language Models (LLMs) have significantly accelerated progress in this field. However, existing studies typically focus on a single query language, resulting in methods with limited generalizability across different languages. In this paper, we formally define the Text-to-Query task paradigm, unifying semantic parsing tasks across various query languages. We identify query skeletons as a shared optimization target of Text-to-Query tasks, and propose a general dynamic data augmentation framework that explicitly diagnoses model-specific weaknesses in handling these skeletons to synthesize targeted training data. Experiments on four Text-to-Query benchmarks demonstrate that our method achieves state-of-the-art performance using only a small amount of synthesized data, highlighting the efficiency and generality of our approach and laying a solid foundation for unified research on Text-to-Query tasks. We release our code at https://github.com/jjjycaptain/Skeletron.

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