@inproceedings{stormer-etal-2023-sam,
title = "{S}am Miller at {S}em{E}val-2023 Task 5: Classification and Type-specific Spoiler Extraction Using {XLNET} and Other Transformer Models",
author = {St{\"o}rmer, Pia and
Esser, Tobias and
Thomasius, Patrick},
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.169",
doi = "10.18653/v1/2023.semeval-1.169",
pages = "1217--1224",
abstract = "This paper proposes an approach to classify andan approach to generate spoilers for clickbaitarticles and posts. For the spoiler classification,XLNET was trained to fine-tune a model. Withan accuracy of 0.66, 2 out of 3 spoilers arepredicted accurately. The spoiler generationapproach involves preprocessing the clickbaittext and post-processing the output to fit thespoiler type. The approach is evaluated on atest dataset of 1000 posts, with the best resultfor spoiler generation achieved by fine-tuninga RoBERTa Large model with a small learningrate and sample size, reaching a BLEU scoreof 0.311. The paper provides an overview ofthe models and techniques used and discussesthe experimental setup.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="stormer-etal-2023-sam">
<titleInfo>
<title>Sam Miller at SemEval-2023 Task 5: Classification and Type-specific Spoiler Extraction Using XLNET and Other Transformer Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Pia</namePart>
<namePart type="family">Störmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tobias</namePart>
<namePart type="family">Esser</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Patrick</namePart>
<namePart type="family">Thomasius</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">A</namePart>
<namePart type="given">Seza</namePart>
<namePart type="family">Doğruöz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Da San Martino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harish</namePart>
<namePart type="family">Tayyar Madabushi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ritesh</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Sartori</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper proposes an approach to classify andan approach to generate spoilers for clickbaitarticles and posts. For the spoiler classification,XLNET was trained to fine-tune a model. Withan accuracy of 0.66, 2 out of 3 spoilers arepredicted accurately. The spoiler generationapproach involves preprocessing the clickbaittext and post-processing the output to fit thespoiler type. The approach is evaluated on atest dataset of 1000 posts, with the best resultfor spoiler generation achieved by fine-tuninga RoBERTa Large model with a small learningrate and sample size, reaching a BLEU scoreof 0.311. The paper provides an overview ofthe models and techniques used and discussesthe experimental setup.</abstract>
<identifier type="citekey">stormer-etal-2023-sam</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.169</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.169</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>1217</start>
<end>1224</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Sam Miller at SemEval-2023 Task 5: Classification and Type-specific Spoiler Extraction Using XLNET and Other Transformer Models
%A Störmer, Pia
%A Esser, Tobias
%A Thomasius, Patrick
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F stormer-etal-2023-sam
%X This paper proposes an approach to classify andan approach to generate spoilers for clickbaitarticles and posts. For the spoiler classification,XLNET was trained to fine-tune a model. Withan accuracy of 0.66, 2 out of 3 spoilers arepredicted accurately. The spoiler generationapproach involves preprocessing the clickbaittext and post-processing the output to fit thespoiler type. The approach is evaluated on atest dataset of 1000 posts, with the best resultfor spoiler generation achieved by fine-tuninga RoBERTa Large model with a small learningrate and sample size, reaching a BLEU scoreof 0.311. The paper provides an overview ofthe models and techniques used and discussesthe experimental setup.
%R 10.18653/v1/2023.semeval-1.169
%U https://aclanthology.org/2023.semeval-1.169
%U https://doi.org/10.18653/v1/2023.semeval-1.169
%P 1217-1224
Markdown (Informal)
[Sam Miller at SemEval-2023 Task 5: Classification and Type-specific Spoiler Extraction Using XLNET and Other Transformer Models](https://aclanthology.org/2023.semeval-1.169) (Störmer et al., SemEval 2023)
ACL