The Natural Language for Optimization (NL4Opt) Competition was created to
investigate methods of extracting the meaning and formulation of an
optimization problem based on its text description. Specifically, the goal of
the competition is to increase the accessibility and usability of optimization
solvers by allowing non-experts to interface with them using natural language.
We separate this challenging goal into two sub-tasks: (1) recognize and label
the semantic entities that correspond to the components of the optimization
problem; (2) generate a meaning representation (i.e., a logical form) of the
problem from its detected problem entities. The first task aims to reduce
ambiguity by detecting and tagging the entities of the optimization problems.
The second task creates an intermediate representation of the linear
programming (LP) problem that is converted into a format that can be used by
commercial solvers. In this report, we present the LP word problem dataset and
shared tasks for the NeurIPS 2022 competition. Furthermore, we present the
winning solutions. Through this competition, we hope to bring interest towards
the development of novel machine learning applications and datasets for
optimization modeling.

利用自然语言处理方法实现优化问题的自动解决，涉及 NL4Opt 比赛和相关任务数据集，以及通过该比赛所展示的相应解决方案。

基于自然语言描述的优化问题制定：NL4Opt 竞赛

NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition,
competitors focus on improving the accessibility and usability of optimization
solvers, with the aim of subtask 1: recognizing the semantic entities that
correspond to the components of the optimization problem; subtask 2: generating
formulations for the optimization problem. In this paper, we present the
solution of our team. First, we treat subtask 1 as a named entity recognition
(NER) problem with the solution pipeline including pre-processing methods,
adversarial training, post-processing methods and ensemble learning. Besides,
we treat subtask 2 as a generation problem with the solution pipeline including
specially designed prompts, adversarial training, post-processing methods and
ensemble learning. Our proposed methods have achieved the F1-score of 0.931 in
subtask 1 and the accuracy of 0.867 in subtask 2, which won the fourth and
third places respectively in this competition. Our code is available at
this https URL.

本文介绍了我们团队在 Natural Language for Optimization (NL4Opt) NeurIPS 2022 比赛中的获奖方案，分别采用了命名实体识别和生成模型等方法，成功解决了对应的子任务，并在比赛中获得了不错的成绩。