@inproceedings{ramamonjison-etal-2023-latex2solver,
title = "{L}a{T}e{X}2{S}olver: a Hierarchical Semantic Parsing of {L}a{T}e{X} Document into Code for an Assistive Optimization Modeling Application",
author = "Ramamonjison, Rindra and
Yu, Timothy and
Xing, Linzi and
Mostajabdaveh, Mahdi and
Li, Xiaorui and
Fu, Xiaojin and
Han, Xiongwei and
Chen, Yuanzhe and
Li, Ren and
Mao, Kun and
Zhang, Yong",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.45",
doi = "10.18653/v1/2023.acl-demo.45",
pages = "471--478",
abstract = "We demonstrate an interactive system to help operations research (OR) practitioners convert the mathematical formulation of optimization problems from TeX document format into the solver modeling language. In practice, a manual translation is cumbersome and time-consuming. Moreover, it requires an in-depth understanding of the problem description and a technical expertise to produce the modeling code. Thus, our proposed system TeX2Solver helps partially automate this conversion and help the users build optimization models more efficiently. In this paper, we describe its interface and the components of the hierarchical parsing system. A video demo walk-through is available online at \url{http://bit.ly/3kuOm3x}",
}
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%0 Conference Proceedings
%T LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application
%A Ramamonjison, Rindra
%A Yu, Timothy
%A Xing, Linzi
%A Mostajabdaveh, Mahdi
%A Li, Xiaorui
%A Fu, Xiaojin
%A Han, Xiongwei
%A Chen, Yuanzhe
%A Li, Ren
%A Mao, Kun
%A Zhang, Yong
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ramamonjison-etal-2023-latex2solver
%X We demonstrate an interactive system to help operations research (OR) practitioners convert the mathematical formulation of optimization problems from TeX document format into the solver modeling language. In practice, a manual translation is cumbersome and time-consuming. Moreover, it requires an in-depth understanding of the problem description and a technical expertise to produce the modeling code. Thus, our proposed system TeX2Solver helps partially automate this conversion and help the users build optimization models more efficiently. In this paper, we describe its interface and the components of the hierarchical parsing system. A video demo walk-through is available online at http://bit.ly/3kuOm3x
%R 10.18653/v1/2023.acl-demo.45
%U https://aclanthology.org/2023.acl-demo.45
%U https://doi.org/10.18653/v1/2023.acl-demo.45
%P 471-478
Markdown (Informal)
[LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application](https://aclanthology.org/2023.acl-demo.45) (Ramamonjison et al., ACL 2023)
ACL
- Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, and Yong Zhang. 2023. LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 471–478, Toronto, Canada. Association for Computational Linguistics.