Xiaoran Fan
2021
abcbpc at SemEval-2021 Task 7: ERNIE-based Multi-task Model for Detecting and Rating Humor and Offense
Chao Pang
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Xiaoran Fan
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Weiyue Su
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Xuyi Chen
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Shuohuan Wang
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Jiaxiang Liu
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Xuan Ouyang
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Shikun Feng
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Yu Sun
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
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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Co-authors
- Chao Pang 1
- Weiyue Su 1
- Xuyi Chen 1
- Shuohuan Wang 1
- Jiaxiang Liu 1
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