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. Recent machine learning methods have shown promise in converting textual problem descriptions to corresponding mathematical formulations. This paper presents an approach that converts linear programming word problems into mathematical formulations. We leverage the named entities in the input and augment the input to highlight these entities. Our approach achieves the highest accuracy among all submissions to the NL4Opt Competition, securing first place in the generation track.

翻译：运筹学致力于将现实世界问题建模并求解为数学优化问题。尽管数学系统的求解可通过分析软件完成，但将问题表述为一组数学运算通常仍需领域专家手动操作。近年来，机器学习方法在将文本问题描述转化为相应数学表述方面展现出潜力。本文提出了一种将线性规划文字问题转化为数学表述的方法。我们利用输入中的命名实体，通过扩展输入内容来突出这些实体。本方法在NL4Opt竞赛的所有提交方案中取得了最高精度，并荣获生成赛道的冠军。