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        <title>Do We Need Language-Specific Fact-Checking Models? The Case of Chinese</title>
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    <abstract>This paper investigates the potential benefits of language-specific fact-checking models, focusing on the case of Chinese using CHEF dataset. To better reflect real-world fact-checking, we first develop a novel Chinese document-level evidence retriever, achieving state-of-the-art performance. We then demonstrate the limitations of translation-based methods and multilingual language models, highlighting the need for language-specific systems. To better analyze token-level biases in different systems, we construct an adversarial dataset based on the CHEF dataset, where each instance has a large word overlap with the original one but holds the opposite veracity label. Experimental results on the CHEF dataset and our adversarial dataset show that our proposed method outperforms translation-based methods and multilingual language models and is more robust toward biases, emphasizing the importance of language-specific fact-checking systems.</abstract>
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%0 Conference Proceedings
%T Do We Need Language-Specific Fact-Checking Models? The Case of Chinese
%A Zhang, Caiqi
%A Guo, Zhijiang
%A Vlachos, Andreas
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-need
%X This paper investigates the potential benefits of language-specific fact-checking models, focusing on the case of Chinese using CHEF dataset. To better reflect real-world fact-checking, we first develop a novel Chinese document-level evidence retriever, achieving state-of-the-art performance. We then demonstrate the limitations of translation-based methods and multilingual language models, highlighting the need for language-specific systems. To better analyze token-level biases in different systems, we construct an adversarial dataset based on the CHEF dataset, where each instance has a large word overlap with the original one but holds the opposite veracity label. Experimental results on the CHEF dataset and our adversarial dataset show that our proposed method outperforms translation-based methods and multilingual language models and is more robust toward biases, emphasizing the importance of language-specific fact-checking systems.
%U https://aclanthology.org/2024.emnlp-main.113
%P 1899-1914

Markdown (Informal)

[Do We Need Language-Specific Fact-Checking Models? The Case of Chinese](https://aclanthology.org/2024.emnlp-main.113) (Zhang et al., EMNLP 2024)

• Do We Need Language-Specific Fact-Checking Models? The Case of Chinese (Zhang et al., EMNLP 2024)

ACL

• Caiqi Zhang, Zhijiang Guo, and Andreas Vlachos. 2024. Do We Need Language-Specific Fact-Checking Models? The Case of Chinese. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1899–1914, Miami, Florida, USA. Association for Computational Linguistics.