Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling
Emilio Villa Cueva, Daniel Vallejo Aldana, Fernando Sánchez Vega, Adrián Pastor López Monroy
Abstract
This paper describes our participation in the Clickbait challenge at SemEval 2023. In this work, we address the Clickbait classification task using transformers models in an ensemble configuration. We tackle the Spoiler Generation task using a two-level ensemble strategy of models trained for extractive QA, and selecting the best K candidates for multi-part spoilers. In the test partitions, our approaches obtained a classification accuracy of 0.716 for classification and a BLEU-4 score of 0.439 for spoiler generation.- Anthology ID:
- 2023.semeval-1.95
- 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:
- 693–699
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.95/
- DOI:
- 10.18653/v1/2023.semeval-1.95
- Cite (ACL):
- Emilio Villa Cueva, Daniel Vallejo Aldana, Fernando Sánchez Vega, and Adrián Pastor López Monroy. 2023. Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 693–699, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling (Villa Cueva et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.95.pdf