In this report, I address auto-formulation of problem description, the task of converting an optimization problem into a canonical representation. I first simplify the auto-formulation task by defining an intermediate representation, then introduce entity tag embedding to utilize a given entity tag information. The ablation study demonstrate the effectiveness of the proposed method, which finally took second place in NeurIPS 2022 NL4Opt competition subtask 2.
Cite
@article{arxiv.2212.03575,
title = {Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Description},
author = {Sanghwan Jang},
journal= {arXiv preprint arXiv:2212.03575},
year = {2022}
}