Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Description
This work addresses the problem of converting optimization problem descriptions into a standard format for researchers and practitioners, but it is incremental as it builds on existing competition tasks.
The authors tackled the auto-formulation of optimization problems into canonical representations by defining an intermediate representation and using entity tag embeddings, achieving second place in the NeurIPS 2022 NL4Opt competition subtask 2.
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.