@inproceedings{kanaani-2024-triple-r,
title = "Triple-{R}: Automatic Reasoning for Fact Verification Using Language Models",
author = "Kanaani, Mohammadamin and
Dadkhah, Sajjad and
Ghorbani, Ali A.",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1463",
pages = "16831--16840",
abstract = "The rise of online social media platforms has made them a popular source of news. However, they are also prone to misinformation and fake news. To combat this, fact-checking is essential to verify the accuracy of claims made on these platforms. However, the existing methods in this field often lack the use of external sources and human-understandable explanations for system decisions. In this paper, we introduce a framework called Triple-R (Retriever, Ranker, Reasoner) that addresses these challenges. The framework uses the Web as an external knowledge source to retrieve relevant evidence for claims and includes a method to generate reasons based on the retrieved evidence for datasets lacking explanations. We then use this modified dataset to fine-tune a causal language model that generates natural language explanations and labels for pairs of retrieved evidence and claims. Our approach aims to improve the transparency and interpretability of fact-checking systems by providing understandable explanations for decision-making processes. We evaluated our method on a popular dataset and demonstrated its performance through an ablation study. The modified dataset is available on the Canadian Institute for Cybersecurity datasets webpage at https://www.unb.ca/cic/datasets/index.html.",
}
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<abstract>The rise of online social media platforms has made them a popular source of news. However, they are also prone to misinformation and fake news. To combat this, fact-checking is essential to verify the accuracy of claims made on these platforms. However, the existing methods in this field often lack the use of external sources and human-understandable explanations for system decisions. In this paper, we introduce a framework called Triple-R (Retriever, Ranker, Reasoner) that addresses these challenges. The framework uses the Web as an external knowledge source to retrieve relevant evidence for claims and includes a method to generate reasons based on the retrieved evidence for datasets lacking explanations. We then use this modified dataset to fine-tune a causal language model that generates natural language explanations and labels for pairs of retrieved evidence and claims. Our approach aims to improve the transparency and interpretability of fact-checking systems by providing understandable explanations for decision-making processes. We evaluated our method on a popular dataset and demonstrated its performance through an ablation study. The modified dataset is available on the Canadian Institute for Cybersecurity datasets webpage at https://www.unb.ca/cic/datasets/index.html.</abstract>
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%0 Conference Proceedings
%T Triple-R: Automatic Reasoning for Fact Verification Using Language Models
%A Kanaani, Mohammadamin
%A Dadkhah, Sajjad
%A Ghorbani, Ali A.
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F kanaani-2024-triple-r
%X The rise of online social media platforms has made them a popular source of news. However, they are also prone to misinformation and fake news. To combat this, fact-checking is essential to verify the accuracy of claims made on these platforms. However, the existing methods in this field often lack the use of external sources and human-understandable explanations for system decisions. In this paper, we introduce a framework called Triple-R (Retriever, Ranker, Reasoner) that addresses these challenges. The framework uses the Web as an external knowledge source to retrieve relevant evidence for claims and includes a method to generate reasons based on the retrieved evidence for datasets lacking explanations. We then use this modified dataset to fine-tune a causal language model that generates natural language explanations and labels for pairs of retrieved evidence and claims. Our approach aims to improve the transparency and interpretability of fact-checking systems by providing understandable explanations for decision-making processes. We evaluated our method on a popular dataset and demonstrated its performance through an ablation study. The modified dataset is available on the Canadian Institute for Cybersecurity datasets webpage at https://www.unb.ca/cic/datasets/index.html.
%U https://aclanthology.org/2024.lrec-main.1463
%P 16831-16840
Markdown (Informal)
[Triple-R: Automatic Reasoning for Fact Verification Using Language Models](https://aclanthology.org/2024.lrec-main.1463) (Kanaani et al., LREC-COLING 2024)
ACL