This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense. This task aims to detect whether the text is humorous and how humorous it is. There are four subtasks in the competition. In this paper, we mainly present our solution, a multi-task learning model based on adversarial examples, for task 1a and 1b. More specifically, we first vectorize the cleaned dataset and add the perturbation to obtain more robust embedding representations. We then correct the loss via the confidence level. Finally, we perform interactive joint learning on multiple tasks to capture the relationship between whether the text is humorous and how humorous it is. The final result shows the effectiveness of our system.

翻译：本文描述 MagicPai 的系统, 用于 SemEval 2021 任务 7 、 7 、 HaHackathon : 检测和评分幽默和防御。 此任务旨在检测文字是否幽默, 以及它有多幽默。 竞争中共有四个子任务 。 在本文中, 我们主要为任务 1a 和 1b 展示我们的解决方案, 即基于对抗性例子的多任务学习模式。 更具体地说, 我们首先将干净的数据集向下传, 并添加扰动, 以获得更强健的嵌入演示。 然后通过信任度纠正损失。 最后, 我们共同学习多个任务, 以捕捉文字是否幽默和多么幽默之间的关系。 最后的结果显示了我们的系统的有效性 。