Rindra Ramamonjison


2024

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Towards Human-aligned Evaluation for Linear Programming Word Problems
Linzi Xing | Xinglu Wang | Yuxi Feng | Zhenan Fan | Jing Xiong | Zhijiang Guo | Xiaojin Fu | Rindra Ramamonjison | Mahdi Mostajabdaveh | Xiongwei Han | Zirui Zhou | Yong Zhang
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Math Word Problem (MWP) is a crucial NLP task aimed at providing solutions for given mathematical descriptions. A notable sub-category of MWP is the Linear Programming Word Problem (LPWP), which holds significant relevance in real-world decision-making and operations research. While the recent rise of generative large language models (LLMs) has brought more advanced solutions to LPWPs, existing evaluation methodologies for this task still diverge from human judgment and face challenges in recognizing mathematically equivalent answers. In this paper, we introduce a novel evaluation metric rooted in graph edit distance, featuring benefits such as permutation invariance and more accurate program equivalence identification. Human evaluations empirically validate the superior efficacy of our proposed metric when particularly assessing LLM-based solutions for LPWP.

2023

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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
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

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

2022

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Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions
Rindra Ramamonjison | Haley Li | Timothy Yu | Shiqi He | Vishnu Rengan | Amin Banitalebi-dehkordi | Zirui Zhou | Yong Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the suggestions. We investigate controlled generation techniques to obtain an automatic suggestion of formulation. Then, we evaluate their effectiveness with a newly created dataset of linear programming problems drawn from various application domains.