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.- Anthology ID:
- 2023.semeval-1.146
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1067–1068
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.146/
- DOI:
- 10.18653/v1/2023.semeval-1.146
- Cite (ACL):
- Nukit Tailor and Radhika Mamidi. 2023. Matt Bai at SemEval-2023 Task 5: Clickbait spoiler classification via BERT. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1067–1068, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Matt Bai at SemEval-2023 Task 5: Clickbait spoiler classification via BERT (Tailor & Mamidi, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.146.pdf