  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.
