Papers by Amani Abumansour
Zenodo (CERN European Organization for Nuclear Research), Mar 17, 2023
PeerJ Computer Science
An important component of an automated fact-checking system is the claim check-worthiness detecti... more An important component of an automated fact-checking system is the claim check-worthiness detection system, which ranks sentences by prioritising them based on their need to be checked. Despite a body of research tackling the task, previous research has overlooked the challenging nature of identifying check-worthy claims across different topics. In this article, we assess and quantify the challenge of detecting check-worthy claims for new, unseen topics. After highlighting the problem, we propose the AraCWA model to mitigate the performance deterioration when detecting check-worthy claims across topics. The AraCWA model enables boosting the performance for new topics by incorporating two components for few-shot learning and data augmentation. Using a publicly available dataset of Arabic tweets consisting of 14 different topics, we demonstrate that our proposed data augmentation strategy achieves substantial improvements across topics overall, where the extent of the improvement vari...
arXiv (Cornell University), Dec 16, 2022
An important component of an automated fact-checking system is the claim check-worthiness detecti... more An important component of an automated fact-checking system is the claim check-worthiness detection system, which ranks sentences by prioritising them based on their need to be checked. Despite a body of research tackling the task, previous research has overlooked the challenging nature of identifying check-worthy claims across different topics. In this paper, we assess and quantify the challenge of detecting check-worthy claims for new, unseen topics. After highlighting the problem, we propose the AraCWA model to mitigate the performance deterioration when detecting check-worthy claims across topics. The AraCWA model enables boosting the performance for new topics by incorporating two components for few-shot learning and data augmentation. Using a publicly available dataset of Arabic tweets consisting of 14 different topics, we demonstrate that our proposed data augmentation strategy achieves substantial improvements across topics overall, where the extent of the improvement varies across topics. Further, we analyse the semantic similarities between topics, suggesting that the similarity metric could be used as a proxy to determine the difficulty level of an unseen topic prior to undertaking the task of labelling the underlying sentences.
arXiv: Computation and Language, Sep 23, 2021
As online false information continues to grow, automated fact-checking has gained an increasing a... more As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This paper reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.
This paper describes our submission to the CheckThat! Lab at CLEF 2021, where we participated in ... more This paper describes our submission to the CheckThat! Lab at CLEF 2021, where we participated in Subtask 1A (check-worthy claim detection) in Arabic. We introduce our approach to estimate the checkworthiness of tweets as a ranking task. In our approach, we propose to fine-tune state-of-art transformer based models for Arabic such as AraBERTv0.2-base as well as to leverage additional training data from last year's shared task (CheckThat! Lab 2020) along with the dataset provided this year. According to the official evaluation, our submission obtained a joint 4 th position in the competition where seven other groups participated.
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Papers by Amani Abumansour