论文标题
在输入中突出显示命名实体，以自动制定优化问题
Highlighting Named Entities in Input for Auto-Formulation of Optimization Problems
论文作者
论文摘要
操作研究涉及建模和解决现实世界问题作为数学优化问题。虽然求解数学系统是通过分析软件来完成的，但是将问题作为一组数学操作进行了制定，通常是由域专家手动完成的。最近的机器学习方法显示了将文本问题描述转换为相应数学公式的有望。本文提出了一种将线性编程单词问题转换为数学公式的方法。我们利用输入中的指定实体，并增加输入以突出这些实体。我们的方法在NL4OPT竞赛的所有提交中都达到了最高的准确性，并确保了世代的第一名。
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.
