@inproceedings{pandey-etal-2021-endtimes,
title = "{E}nd{T}imes at {S}em{E}val-2021 Task 7: Detecting and Rating Humor and Offense with {BERT} and Ensembles",
author = "Pandey, Chandan Kumar and
Singh, Chirag and
Mangla, Karan",
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-1.172/",
doi = "10.18653/v1/2021.semeval-1.172",
pages = "1215--1220",
abstract = "This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble approach to solve various sub-tasks that were part of the challenge. We also present a comparative analysis of various models we tried. Our method was ranked 4th in Humor Controversy Detection, 8th in Humor Detection, 19th in Average Offense Score prediction and 40th in Average Humor Score prediction globally. F1 score obtained for Humor classification was 0.9655 and for Controversy detection it was 0.6261. Our user name on the leader board is ThisIstheEnd and team name is EndTimes."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pandey-etal-2021-endtimes">
<titleInfo>
<title>EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chandan</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Pandey</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chirag</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Karan</namePart>
<namePart type="family">Mangla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natalie</namePart>
<namePart type="family">Schluter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guy</namePart>
<namePart type="family">Emerson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble approach to solve various sub-tasks that were part of the challenge. We also present a comparative analysis of various models we tried. Our method was ranked 4th in Humor Controversy Detection, 8th in Humor Detection, 19th in Average Offense Score prediction and 40th in Average Humor Score prediction globally. F1 score obtained for Humor classification was 0.9655 and for Controversy detection it was 0.6261. Our user name on the leader board is ThisIstheEnd and team name is EndTimes.</abstract>
<identifier type="citekey">pandey-etal-2021-endtimes</identifier>
<identifier type="doi">10.18653/v1/2021.semeval-1.172</identifier>
<location>
<url>https://aclanthology.org/2021.semeval-1.172/</url>
</location>
<part>
<date>2021-08</date>
<extent unit="page">
<start>1215</start>
<end>1220</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles
%A Pandey, Chandan Kumar
%A Singh, Chirag
%A Mangla, Karan
%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 pandey-etal-2021-endtimes
%X This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble approach to solve various sub-tasks that were part of the challenge. We also present a comparative analysis of various models we tried. Our method was ranked 4th in Humor Controversy Detection, 8th in Humor Detection, 19th in Average Offense Score prediction and 40th in Average Humor Score prediction globally. F1 score obtained for Humor classification was 0.9655 and for Controversy detection it was 0.6261. Our user name on the leader board is ThisIstheEnd and team name is EndTimes.
%R 10.18653/v1/2021.semeval-1.172
%U https://aclanthology.org/2021.semeval-1.172/
%U https://doi.org/10.18653/v1/2021.semeval-1.172
%P 1215-1220
Markdown (Informal)
[EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles](https://aclanthology.org/2021.semeval-1.172/) (Pandey et al., SemEval 2021)
ACL