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
this https URL.

本文介绍了我们团队在 Natural Language for Optimization (NL4Opt) NeurIPS 2022 比赛中的获奖方案，分别采用了命名实体识别和生成模型等方法，成功解决了对应的子任务，并在比赛中获得了不错的成绩。

一种自动化制定优化问题的新方法

A Novel Approach for Auto-Formulation of Optimization Problems

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.

该研究提出一种自动将优化问题转化为规范表述的方法，并引入实体标签嵌入来利用给定的实体标签信息，该方法通过消融实验证明了其有效性，在 NeurIPS 2022 NL4Opt 竞赛子任务 2 中位列第二名。

标签嵌入和明确定义中间表示改善问题描述的自动生成

Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Description

Accurately forecasting the weather is an important task, as many real-world
processes and decisions depend on future meteorological conditions. The NeurIPS
2022 challenge entitled Weather4cast poses the problem of predicting rainfall
events for the next eight hours given the preceding hour of satellite
observations as a context. Motivated by the recent success of transformer-based
architectures in computer vision, we implement and propose two methodologies
based on this architecture to tackle this challenge. We find that ensembling
different transformers with some baseline models achieves the best performance
we could measure on the unseen test data. Our approach has been ranked 3rd in
the competition.

本文介绍了通过使用基于 transformer 的算法来解决 NeurIPS 2022 中的 Weather4cast 挑战，预测在接下来的 8 小时内的降雨事件，并通过将不同的 transformer 和基线模型进行集合，获得了最好的性能。