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：运筹学旨在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已通过分析软件实现，但将问题转化为数学运算集合通常仍需领域专家手工完成。近期机器学习方法在将文本问题描述转化为对应数学公式方面展现出潜力。本文提出一种将线性规划文字题转化为数学公式的方法。我们利用输入中的命名实体，通过增强输入内容来突出显示这些实体。本方法在NL4Opt竞赛的所有提交中取得最高精度，斩获生成赛道第一名。