@inproceedings{ma-etal-2021-magicpai,
title = "{M}agic{P}ai at {S}em{E}val-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training",
author = "Ma, Jian and
Xie, Shuyi and
Yang, Haiqin and
Jiang, Lianxin and
Zhou, Mengyuan and
Ruan, Xiaoyi and
Mo, Yang",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.162",
doi = "10.18653/v1/2021.semeval-1.162",
pages = "1153--1159",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training
%A Ma, Jian
%A Xie, Shuyi
%A Yang, Haiqin
%A Jiang, Lianxin
%A Zhou, Mengyuan
%A Ruan, Xiaoyi
%A Mo, Yang
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F ma-etal-2021-magicpai
%X 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.
%R 10.18653/v1/2021.semeval-1.162
%U https://aclanthology.org/2021.semeval-1.162
%U https://doi.org/10.18653/v1/2021.semeval-1.162
%P 1153-1159
Markdown (Informal)
[MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training](https://aclanthology.org/2021.semeval-1.162) (Ma et al., SemEval 2021)
ACL