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 https://github.com/bigdata-ustc/nl4opt.

翻译：在NeurIPS 2022自然语言优化（NL4Opt）竞赛中，参赛者专注于提升优化求解器的可访问性与易用性，目标包括子任务1：识别与优化问题组成部分对应的语义实体；子任务2：生成优化问题的数学形式化描述。本文介绍了我们团队的解决方案。首先，我们将子任务1视为命名实体识别（NER）问题，其解决方案流程包括预处理方法、对抗训练、后处理方法及集成学习。此外，我们将子任务2视为生成问题，其解决方案流程包括专门设计的提示词（prompt）、对抗训练、后处理方法及集成学习。我们提出的方法在子任务1中取得0.931的F1分数，在子任务2中达到0.867的准确率，分别获得该竞赛的第四名和第三名。我们的代码已开源至https://github.com/bigdata-ustc/nl4opt。