@inproceedings{zylich-etal-2021-amherst685,
title = "Amherst685 at {S}em{E}val-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense",
author = "Zylich, Brian and
Gugnani, Akshay and
Brookman, Gabriel and
Samoray, Nicholas",
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-1.168",
doi = "10.18653/v1/2021.semeval-1.168",
pages = "1190--1195",
abstract = "This paper describes our submission to theSemEval{'}21: Task 7- HaHackathon: Detecting and Rating Humor and Offense. In this challenge, we explore intermediate finetuning, backtranslation augmentation, multitask learning, and ensembling of different language models. Curiously, intermediate finetuning and backtranslation do not improve performance, while multitask learning and ensembling do improve performance. We explore why intermediate finetuning and backtranslation do not provide the same benefit as other natural language processing tasks and offer insight into the errors that our model makes. Our best performing system ranks 7th on Task 1bwith an RMSE of 0.5339",
}
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<abstract>This paper describes our submission to theSemEval’21: Task 7- HaHackathon: Detecting and Rating Humor and Offense. In this challenge, we explore intermediate finetuning, backtranslation augmentation, multitask learning, and ensembling of different language models. Curiously, intermediate finetuning and backtranslation do not improve performance, while multitask learning and ensembling do improve performance. We explore why intermediate finetuning and backtranslation do not provide the same benefit as other natural language processing tasks and offer insight into the errors that our model makes. Our best performing system ranks 7th on Task 1bwith an RMSE of 0.5339</abstract>
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%0 Conference Proceedings
%T Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense
%A Zylich, Brian
%A Gugnani, Akshay
%A Brookman, Gabriel
%A Samoray, Nicholas
%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 zylich-etal-2021-amherst685
%X This paper describes our submission to theSemEval’21: Task 7- HaHackathon: Detecting and Rating Humor and Offense. In this challenge, we explore intermediate finetuning, backtranslation augmentation, multitask learning, and ensembling of different language models. Curiously, intermediate finetuning and backtranslation do not improve performance, while multitask learning and ensembling do improve performance. We explore why intermediate finetuning and backtranslation do not provide the same benefit as other natural language processing tasks and offer insight into the errors that our model makes. Our best performing system ranks 7th on Task 1bwith an RMSE of 0.5339
%R 10.18653/v1/2021.semeval-1.168
%U https://aclanthology.org/2021.semeval-1.168
%U https://doi.org/10.18653/v1/2021.semeval-1.168
%P 1190-1195
Markdown (Informal)
[Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense](https://aclanthology.org/2021.semeval-1.168) (Zylich et al., SemEval 2021)
ACL