@inproceedings{tailor-mamidi-2023-matt,
title = "Matt {B}ai at {S}em{E}val-2023 Task 5: Clickbait spoiler classification via {BERT}",
author = "Tailor, Nukit and
Mamidi, Radhika",
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-1.146",
doi = "10.18653/v1/2023.semeval-1.146",
pages = "1067--1068",
abstract = "The Clickbait Spoiling shared task aims at tackling two aspects of spoiling: classifying the spoiler type based on its length and generating the spoiler. This paper focuses on the task of classifying the spoiler type. Better classification of the spoiler type would eventually help in generating a better spoiler for the post. We use BERT-base (cased) to classify the clickbait posts. The model achieves a balanced accuracy of 0.63 as we give only the post content as the input to our model instead of the concatenation of the post title and post content to find out the differences that the post title might be bringing in.",
}
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<abstract>The Clickbait Spoiling shared task aims at tackling two aspects of spoiling: classifying the spoiler type based on its length and generating the spoiler. This paper focuses on the task of classifying the spoiler type. Better classification of the spoiler type would eventually help in generating a better spoiler for the post. We use BERT-base (cased) to classify the clickbait posts. The model achieves a balanced accuracy of 0.63 as we give only the post content as the input to our model instead of the concatenation of the post title and post content to find out the differences that the post title might be bringing in.</abstract>
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%0 Conference Proceedings
%T Matt Bai at SemEval-2023 Task 5: Clickbait spoiler classification via BERT
%A Tailor, Nukit
%A Mamidi, Radhika
%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 tailor-mamidi-2023-matt
%X The Clickbait Spoiling shared task aims at tackling two aspects of spoiling: classifying the spoiler type based on its length and generating the spoiler. This paper focuses on the task of classifying the spoiler type. Better classification of the spoiler type would eventually help in generating a better spoiler for the post. We use BERT-base (cased) to classify the clickbait posts. The model achieves a balanced accuracy of 0.63 as we give only the post content as the input to our model instead of the concatenation of the post title and post content to find out the differences that the post title might be bringing in.
%R 10.18653/v1/2023.semeval-1.146
%U https://aclanthology.org/2023.semeval-1.146
%U https://doi.org/10.18653/v1/2023.semeval-1.146
%P 1067-1068
Markdown (Informal)
[Matt Bai at SemEval-2023 Task 5: Clickbait spoiler classification via BERT](https://aclanthology.org/2023.semeval-1.146) (Tailor & Mamidi, SemEval 2023)
ACL