Adaptive and Robust Translation from Natural Language to Multi-model Query Languages
Gengyuan Shi, Chaokun Wang, Liu Yabin, Jiawei Ren
Abstract
Multi-model databases and polystore systems are increasingly studied for managing multi-model data holistically. As their primary interface, multi-model query languages (MMQLs) often exhibit complex grammars, highlighting the need for effective Text-to-MMQL translation methods. Despite advances in natural language translation, no effective solutions for Text-to-MMQL exist. To address this gap, we formally define the Text-to-MMQL task and present the first Text-to-MMQL dataset involving three representative MMQLs. We propose an adaptive Text-to-MMQL framework that includes both a schema embedding module for capturing multi-model schema information and an MMQL representation strategy to generate concise intermediate query formats with error correction in generated queries. Experimental results show that the proposed framework achieves over a 9% accuracy improvement over our adapted baseline methods.
BibTeX
@inproceedings{shi-etal-2025-adaptive,
title = "Adaptive and Robust Translation from Natural Language to Multi-model Query Languages",
author = "Shi, Gengyuan and
Wang, Chaokun and
Yabin, Liu and
Ren, Jiawei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.776/",
doi = "10.18653/v1/2025.acl-long.776",
pages = "15950--15965",
ISBN = "979-8-89176-251-0"
}