@inproceedings{song-etal-2021-deepblueai-semeval,
title = "{D}eep{B}lue{AI} at {S}em{E}val-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods",
author = "Song, Bingyan and
Pan, Chunguang and
Wang, Shengguang and
Luo, Zhipeng",
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-1.158",
doi = "10.18653/v1/2021.semeval-1.158",
pages = "1130--1134",
abstract = "This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.",
}
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<abstract>This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.</abstract>
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%0 Conference Proceedings
%T DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods
%A Song, Bingyan
%A Pan, Chunguang
%A Wang, Shengguang
%A Luo, Zhipeng
%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 song-etal-2021-deepblueai-semeval
%X This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.
%R 10.18653/v1/2021.semeval-1.158
%U https://aclanthology.org/2021.semeval-1.158
%U https://doi.org/10.18653/v1/2021.semeval-1.158
%P 1130-1134
Markdown (Informal)
[DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods](https://aclanthology.org/2021.semeval-1.158) (Song et al., SemEval 2021)
ACL