@inproceedings{zhao-tao-2021-zyj-semeval,
title = "{ZYJ} at {S}em{E}val-2021 Task 7: {H}a{H}ackathon: Detecting and Rating Humor and Offense with {ALBERT}-Based Model",
author = "Zhao, Yingjia and
Tao, Xin",
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-1.165",
doi = "10.18653/v1/2021.semeval-1.165",
pages = "1175--1178",
abstract = "This article introduces the submission of subtask 1 and subtask 2 that we participate in SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense, we use a model based on ALBERT that uses ALBERT as the module for extracting text features. We modify the upper layer structure by adding specific networks to better summarize the semantic information. Finally, our system achieves an F-Score of 0.9348 in subtask 1a, RMSE of 0.7214 in subtask 1b, F-Score of 0.4603 in subtask 1c, and RMSE of 0.5204 in subtask 2.",
}
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<abstract>This article introduces the submission of subtask 1 and subtask 2 that we participate in SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense, we use a model based on ALBERT that uses ALBERT as the module for extracting text features. We modify the upper layer structure by adding specific networks to better summarize the semantic information. Finally, our system achieves an F-Score of 0.9348 in subtask 1a, RMSE of 0.7214 in subtask 1b, F-Score of 0.4603 in subtask 1c, and RMSE of 0.5204 in subtask 2.</abstract>
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%0 Conference Proceedings
%T ZYJ at SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense with ALBERT-Based Model
%A Zhao, Yingjia
%A Tao, Xin
%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 zhao-tao-2021-zyj-semeval
%X This article introduces the submission of subtask 1 and subtask 2 that we participate in SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense, we use a model based on ALBERT that uses ALBERT as the module for extracting text features. We modify the upper layer structure by adding specific networks to better summarize the semantic information. Finally, our system achieves an F-Score of 0.9348 in subtask 1a, RMSE of 0.7214 in subtask 1b, F-Score of 0.4603 in subtask 1c, and RMSE of 0.5204 in subtask 2.
%R 10.18653/v1/2021.semeval-1.165
%U https://aclanthology.org/2021.semeval-1.165
%U https://doi.org/10.18653/v1/2021.semeval-1.165
%P 1175-1178
Markdown (Informal)
[ZYJ at SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense with ALBERT-Based Model](https://aclanthology.org/2021.semeval-1.165) (Zhao & Tao, SemEval 2021)
ACL