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:运作研究旨在将现实世界问题建模并解决为数学优化问题。尽管通过分析软件完成数学系统求解,但通常将问题公式化为一组数学运算是由该领域专家手动完成的。最近的机器学习方法已经展现出在将文本问题描述转化为相应的数学公式方面的潜力。本文提出了一种将线性规划单词问题转化为数学公式的方法。我们利用输入中的命名实体并扩充输入以突出这些实体。我们的方法在NL4Opt竞赛中在所有提交中获得最高准确度,在生成轨迹中获得第一名。