Operations research deals with modeling and solving real-world problems as mathematical optimization problems. While solving mathematical systems is accomplished by analytical software, formulating a problem as a set of mathematical operations has been typically done manually by domain experts. However, recent machine learning models have shown promise in converting textual problem descriptions to corresponding mathematical formulations. In this paper, we present an approach that converts linear programming word problems into meaning representations that are structured and can be used by optimization solvers. Our approach uses the named entity-based enrichment to augment the input and achieves state-of-the-art accuracy, winning the second task of the NL4Opt competition (https://nl4opt.github.io).

翻译：操作研究涉及模拟和解决数学优化问题等现实世界问题。在通过分析软件解决数学系统的同时,由于一组数学操作通常是由域专家手工完成的,而拟订问题通常由域专家手工完成。然而,最近的机器学习模型显示,将文字问题描述转换成相应的数学配方有希望。在本文中,我们提出了一个方法,将线性编程词问题转换成由优化解答者结构化和可以使用的表示方式。我们的方法利用以实体为基础的浓缩来增加投入并实现最新准确性,从而赢得NL4Opt竞争的第二项任务(https://nl4opt.github.io)。