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.

翻译：在本报告中,我谈到问题描述的自动表述,即将优化问题转化为典型代表的任务。我首先通过界定中间代表来简化自动表述任务,然后采用实体标签嵌入,以利用特定实体标签信息。反通货膨胀研究显示了拟议方法的有效性,该方法最终在NeurIPS 2022 NL4Opt竞争子任务2中名列第二。