@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",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Adaptive and Robust Translation from Natural Language to Multi-model Query Languages
%A Shi, Gengyuan
%A Wang, Chaokun
%A Yabin, Liu
%A Ren, Jiawei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F shi-etal-2025-adaptive
%X 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.
%R 10.18653/v1/2025.acl-long.776
%U https://aclanthology.org/2025.acl-long.776/
%U https://doi.org/10.18653/v1/2025.acl-long.776
%P 15950-15965
Markdown (Informal)
[Adaptive and Robust Translation from Natural Language to Multi-model Query Languages](https://aclanthology.org/2025.acl-long.776/) (Shi et al., ACL 2025)
ACL