A Novel Approach for Auto-Formulation of Optimization Problems
This work addresses the accessibility of optimization solvers for users by improving automated problem formulation, but it is incremental as it builds on existing competition tasks and methods.
The paper tackled the problem of automatically formulating optimization problems from natural language by participating in the NL4Opt competition, achieving an F1-score of 0.931 in entity recognition and an accuracy of 0.867 in formulation generation, placing fourth and third respectively.
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.