HYBRINFOX at CheckThat! 2024--Task 1: Enhancing Language Models with Structured Information for Check-Worthiness Estimation
G Faye, M Casanova, B Icard, J Chanson… - arXiv preprint arXiv …, 2024 - arxiv.org
arXiv preprint arXiv:2407.03850, 2024•arxiv.org
This paper summarizes the experiments and results of the HYBRINFOX team for the
CheckThat! 2024-Task 1 competition. We propose an approach enriching Language Models
such as RoBERTa with embeddings produced by triples (subject; predicate; object)
extracted from the text sentences. Our analysis of the developmental data shows that this
method improves the performance of Language Models alone. On the evaluation data, its
best performance was in English, where it achieved an F1 score of 71.1 and ranked 12th out …
CheckThat! 2024-Task 1 competition. We propose an approach enriching Language Models
such as RoBERTa with embeddings produced by triples (subject; predicate; object)
extracted from the text sentences. Our analysis of the developmental data shows that this
method improves the performance of Language Models alone. On the evaluation data, its
best performance was in English, where it achieved an F1 score of 71.1 and ranked 12th out …
This paper summarizes the experiments and results of the HYBRINFOX team for the CheckThat! 2024 - Task 1 competition. We propose an approach enriching Language Models such as RoBERTa with embeddings produced by triples (subject ; predicate ; object) extracted from the text sentences. Our analysis of the developmental data shows that this method improves the performance of Language Models alone. On the evaluation data, its best performance was in English, where it achieved an F1 score of 71.1 and ranked 12th out of 27 candidates. On the other languages (Dutch and Arabic), it obtained more mixed results. Future research tracks are identified toward adapting this processing pipeline to more recent Large Language Models.
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