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.- Anthology ID:
- 2023.semeval-1.169
- 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:
- 1217–1224
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.169
- DOI:
- 10.18653/v1/2023.semeval-1.169
- Cite (ACL):
- Pia Störmer, Tobias Esser, and Patrick Thomasius. 2023. Sam Miller at SemEval-2023 Task 5: Classification and Type-specific Spoiler Extraction Using XLNET and Other Transformer Models. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1217–1224, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Sam Miller at SemEval-2023 Task 5: Classification and Type-specific Spoiler Extraction Using XLNET and Other Transformer Models (Störmer et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.semeval-1.169.pdf