Andreas Kruff


2023

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Billie-Newman at SemEval-2023 Task 5: Clickbait Classification and Question Answering with Pre-Trained Language Models, Named Entity Recognition and Rule-Based Approaches
Andreas Kruff | Anh Huy Tran
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

In this paper, we describe the implementations of our systems for the SemEval-2023 Task 5 ‘Clickbait Spoiling’, which involves the classification of clickbait posts in sub-task 1 and the spoiler generation and question answering of clickbait posts in sub-task 2, ultimately achieving a balanced accuracy of 0.593 and a BLEU score of 0.322 on the test datasets in sub-task 1 and sub-task 2 respectively. For this, we propose the usage of RoBERTa transformer models and modify them for each specific downstream task. In sub-task 1, we use the pre-trained RoBERTa model and use it in conjunction with NER, a spoiler-title ratio, a regex check for enumerations and lists and the use of input reformulation. In sub-task 2, we propose the usage of the RoBERTa-SQuAD2.0 model for extractive question answering in combination with a contextual rule-based approach for multi-type spoilers in order to generate spoiler answers.
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