@inproceedings{pang-etal-2021-abcbpc,
title = "abcbpc at {S}em{E}val-2021 Task 7: {ERNIE}-based Multi-task Model for Detecting and Rating Humor and Offense",
author = "Pang, Chao and
Fan, Xiaoran and
Su, Weiyue and
Chen, Xuyi and
Wang, Shuohuan and
Liu, Jiaxiang and
Ouyang, Xuan and
Feng, Shikun and
Sun, Yu",
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-1.35/",
doi = "10.18653/v1/2021.semeval-1.35",
pages = "286--289",
abstract = "This paper describes our system participated in Task 7 of SemEval-2021: Detecting and Rating Humor and Offense. The task is designed to detect and score humor and offense which are influenced by subjective factors. In order to obtain semantic information from a large amount of unlabeled data, we applied unsupervised pre-trained language models. By conducting research and experiments, we found that the ERNIE 2.0 and DeBERTa pre-trained models achieved impressive performance in various subtasks. Therefore, we applied the above pre-trained models to fine-tune the downstream neural network. In the process of fine-tuning the model, we adopted multi-task training strategy and ensemble learning method. Based on the above strategy and method, we achieved RMSE of 0.4959 for subtask 1b, and finally won the first place."
}
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<abstract>This paper describes our system participated in Task 7 of SemEval-2021: Detecting and Rating Humor and Offense. The task is designed to detect and score humor and offense which are influenced by subjective factors. In order to obtain semantic information from a large amount of unlabeled data, we applied unsupervised pre-trained language models. By conducting research and experiments, we found that the ERNIE 2.0 and DeBERTa pre-trained models achieved impressive performance in various subtasks. Therefore, we applied the above pre-trained models to fine-tune the downstream neural network. In the process of fine-tuning the model, we adopted multi-task training strategy and ensemble learning method. Based on the above strategy and method, we achieved RMSE of 0.4959 for subtask 1b, and finally won the first place.</abstract>
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%0 Conference Proceedings
%T abcbpc at SemEval-2021 Task 7: ERNIE-based Multi-task Model for Detecting and Rating Humor and Offense
%A Pang, Chao
%A Fan, Xiaoran
%A Su, Weiyue
%A Chen, Xuyi
%A Wang, Shuohuan
%A Liu, Jiaxiang
%A Ouyang, Xuan
%A Feng, Shikun
%A Sun, Yu
%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 pang-etal-2021-abcbpc
%X This paper describes our system participated in Task 7 of SemEval-2021: Detecting and Rating Humor and Offense. The task is designed to detect and score humor and offense which are influenced by subjective factors. In order to obtain semantic information from a large amount of unlabeled data, we applied unsupervised pre-trained language models. By conducting research and experiments, we found that the ERNIE 2.0 and DeBERTa pre-trained models achieved impressive performance in various subtasks. Therefore, we applied the above pre-trained models to fine-tune the downstream neural network. In the process of fine-tuning the model, we adopted multi-task training strategy and ensemble learning method. Based on the above strategy and method, we achieved RMSE of 0.4959 for subtask 1b, and finally won the first place.
%R 10.18653/v1/2021.semeval-1.35
%U https://aclanthology.org/2021.semeval-1.35/
%U https://doi.org/10.18653/v1/2021.semeval-1.35
%P 286-289
Markdown (Informal)
[abcbpc at SemEval-2021 Task 7: ERNIE-based Multi-task Model for Detecting and Rating Humor and Offense](https://aclanthology.org/2021.semeval-1.35/) (Pang et al., SemEval 2021)
ACL
- Chao Pang, Xiaoran Fan, Weiyue Su, Xuyi Chen, Shuohuan Wang, Jiaxiang Liu, Xuan Ouyang, Shikun Feng, and Yu Sun. 2021. abcbpc at SemEval-2021 Task 7: ERNIE-based Multi-task Model for Detecting and Rating Humor and Offense. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 286–289, Online. Association for Computational Linguistics.