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.
@article{arxiv.2302.04643,
title = {A Novel Approach for Auto-Formulation of Optimization Problems},
author = {Yuting Ning and Jiayu Liu and Longhu Qin and Tong Xiao and Shangzi Xue and Zhenya Huang and Qi Liu and Enhong Chen and Jinze Wu},
journal= {arXiv preprint arXiv:2302.04643},
year = {2023}
}