AutoDSL: Automated domain-specific language design for structural representation of procedures with constraints

Yu-Zhe Shi, Haofei Hou, Zhangqian Bi, Fanxu Meng, Xiang Wei, Lecheng Ruan, Qining Wang


Abstract
Accurate representation of procedures in restricted scenarios, such as non-standardized scientific experiments, requires precise depiction of constraints. Unfortunately, Domain-specific Language (DSL), as an effective tool to express constraints structurally, often requires case-by-case hand-crafting, necessitating customized, labor-intensive efforts. To overcome this challenge, we introduce the AutoDSL framework to automate DSL-based constraint design across various domains. Utilizing domain specified experimental protocol corpora, AutoDSL optimizes syntactic constraints and abstracts semantic constraints. Quantitative and qualitative analyses of the DSLs designed by AutoDSL across five distinct domains highlight its potential as an auxiliary module for language models, aiming to improve procedural planning and execution.
Anthology ID:
2024.acl-long.659
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12177–12214
Language:
URL:
https://aclanthology.org/2024.acl-long.659
DOI:
Bibkey:
Cite (ACL):
Yu-Zhe Shi, Haofei Hou, Zhangqian Bi, Fanxu Meng, Xiang Wei, Lecheng Ruan, and Qining Wang. 2024. AutoDSL: Automated domain-specific language design for structural representation of procedures with constraints. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12177–12214, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
AutoDSL: Automated domain-specific language design for structural representation of procedures with constraints (Shi et al., ACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.acl-long.659.pdf