@inproceedings{birkenheuer-etal-2023-sabrina,
title = "Sabrina Spellman at {S}em{E}val-2023 Task 5: Discover the Shocking Truth Behind this Composite Approach to Clickbait Spoiling!",
author = "Birkenheuer, Simon and
Drechsel, Jonathan and
Justen, Paul and
Phlmann, Jimmy and
Gonsior, Julius and
Reusch, Anja",
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-1.134",
doi = "10.18653/v1/2023.semeval-1.134",
pages = "969--977",
abstract = "This paper describes an approach to automat- ically close the knowledge gap of Clickbait- Posts via a transformer model trained for Question-Answering, augmented by a task- specific post-processing step. This was part of the SemEval 2023 Clickbait shared task (Frbe et al., 2023a) - specifically task 2. We devised strategies to improve the existing model to fit the task better, e.g. with different special mod- els and a post-processor tailored to different inherent challenges of the task. Furthermore, we explored the possibility of expanding the original training data by using strategies from Heuristic Labeling and Semi-Supervised Learn- ing. With those adjustments, we were able to improve the baseline by 9.8 percentage points to a BLEU-4 score of 48.0{\%}.",
}
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%0 Conference Proceedings
%T Sabrina Spellman at SemEval-2023 Task 5: Discover the Shocking Truth Behind this Composite Approach to Clickbait Spoiling!
%A Birkenheuer, Simon
%A Drechsel, Jonathan
%A Justen, Paul
%A Phlmann, Jimmy
%A Gonsior, Julius
%A Reusch, Anja
%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 birkenheuer-etal-2023-sabrina
%X This paper describes an approach to automat- ically close the knowledge gap of Clickbait- Posts via a transformer model trained for Question-Answering, augmented by a task- specific post-processing step. This was part of the SemEval 2023 Clickbait shared task (Frbe et al., 2023a) - specifically task 2. We devised strategies to improve the existing model to fit the task better, e.g. with different special mod- els and a post-processor tailored to different inherent challenges of the task. Furthermore, we explored the possibility of expanding the original training data by using strategies from Heuristic Labeling and Semi-Supervised Learn- ing. With those adjustments, we were able to improve the baseline by 9.8 percentage points to a BLEU-4 score of 48.0%.
%R 10.18653/v1/2023.semeval-1.134
%U https://aclanthology.org/2023.semeval-1.134
%U https://doi.org/10.18653/v1/2023.semeval-1.134
%P 969-977
Markdown (Informal)
[Sabrina Spellman at SemEval-2023 Task 5: Discover the Shocking Truth Behind this Composite Approach to Clickbait Spoiling!](https://aclanthology.org/2023.semeval-1.134) (Birkenheuer et al., SemEval 2023)
ACL