@inproceedings{gupta-etal-2021-humor,
title = "Humor@{IITK} at {S}em{E}val-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness",
author = "Gupta, Aishwarya and
Pal, Avik and
Khurana, Bholeshwar and
Tyagi, Lakshay and
Modi, Ashutosh",
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-1.36",
doi = "10.18653/v1/2021.semeval-1.36",
pages = "290--296",
abstract = "Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has several compelling use cases in Recommendation Systems and Personalized Content Moderation. However, due to the lack of an extensive labeled dataset, most prior works in this domain haven{'}t explored large neural models for subjective humor understanding. This paper explores whether large neural models and their ensembles can capture the intricacies associated with humor/offense detection and rating. Our experiments on the SemEval-2021 Task 7: HaHackathon show that we can develop reasonable humor and offense detection systems with such models. Our models are ranked 3rd in subtask 1b and consistently ranked around the top 33{\%} of the leaderboard for the remaining subtasks.",
}
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<abstract>Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has several compelling use cases in Recommendation Systems and Personalized Content Moderation. However, due to the lack of an extensive labeled dataset, most prior works in this domain haven’t explored large neural models for subjective humor understanding. This paper explores whether large neural models and their ensembles can capture the intricacies associated with humor/offense detection and rating. Our experiments on the SemEval-2021 Task 7: HaHackathon show that we can develop reasonable humor and offense detection systems with such models. Our models are ranked 3rd in subtask 1b and consistently ranked around the top 33% of the leaderboard for the remaining subtasks.</abstract>
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%0 Conference Proceedings
%T Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness
%A Gupta, Aishwarya
%A Pal, Avik
%A Khurana, Bholeshwar
%A Tyagi, Lakshay
%A Modi, Ashutosh
%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 gupta-etal-2021-humor
%X Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has several compelling use cases in Recommendation Systems and Personalized Content Moderation. However, due to the lack of an extensive labeled dataset, most prior works in this domain haven’t explored large neural models for subjective humor understanding. This paper explores whether large neural models and their ensembles can capture the intricacies associated with humor/offense detection and rating. Our experiments on the SemEval-2021 Task 7: HaHackathon show that we can develop reasonable humor and offense detection systems with such models. Our models are ranked 3rd in subtask 1b and consistently ranked around the top 33% of the leaderboard for the remaining subtasks.
%R 10.18653/v1/2021.semeval-1.36
%U https://aclanthology.org/2021.semeval-1.36
%U https://doi.org/10.18653/v1/2021.semeval-1.36
%P 290-296
Markdown (Informal)
[Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness](https://aclanthology.org/2021.semeval-1.36) (Gupta et al., SemEval 2021)
ACL