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：在优化自然语言(NL4Opt) NeurIPS 2022 竞争中,竞争者侧重于改善优化解决方案的可及性和可用性,目的是子任务1:承认符合优化问题组成部分的语义实体;子任务2:生成优化问题的配方;在本文件中,我们介绍了我们团队的解决方案。首先,我们把子任务1作为一个名称实体识别(NER)问题对待,解决管道包括预处理方法、对抗性培训、后处理方法和共同学习。此外,我们把子任务2作为一个生成问题对待,解决管道包括专门设计的提示、对抗性培训、后处理方法和共性学习。我们提出的方法已经实现了子任务1中的F1-集合0.931和子任务2中的精确度0.867,这在本次竞争中分别赢得了第四位和第三位。我们的代码可以在 https://github.com/biggata-ustc/nl4opt上查阅。