[Demo] LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application
Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang
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Demo: NLP Applications (demo) Demo Paper
Demo Session 3: NLP Applications (demo) (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 11, 09:00-10:30 (EDT) (America/Toronto)
Global Time: July 11, Demo Session 3 (13:00-14:30 UTC)
TLDR:
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 i...
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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 http://bit.ly/3kuOm3x