Towards Few-Shot Fact-Checking via Perplexity

• URL: http://arxiv.org/abs/2103.09535v1
• Date: Wed, 17 Mar 2021 09:43:19 GMT
• Title: Towards Few-Shot Fact-Checking via Perplexity
• Authors: Nayeon Lee, Yejin Bang, Andrea Madotto, Madian Khabsa, Pascale Fung
• Abstract summary: We propose a new way of utilizing the powerful transfer learning ability of a language model via a perplexity score. Our methodology can already outperform the Major Class baseline by more than absolute 10% on the F1-Macro metric. We construct and publicly release two new fact-checking datasets related to COVID-19.
• Score: 40.11397284006867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot learning has drawn researchers' attention to overcome the problem of data scarcity. Recently, large pre-trained language models have shown great performance in few-shot learning for various downstream tasks, such as question answering and machine translation. Nevertheless, little exploration has been made to achieve few-shot learning for the fact-checking task. However, fact-checking is an important problem, especially when the amount of information online is growing exponentially every day. In this paper, we propose a new way of utilizing the powerful transfer learning ability of a language model via a perplexity score. The most notable strength of our methodology lies in its capability in few-shot learning. With only two training samples, our methodology can already outperform the Major Class baseline by more than absolute 10% on the F1-Macro metric across multiple datasets. Through experiments, we empirically verify the plausibility of the rather surprising usage of the perplexity score in the context of fact-checking and highlight the strength of our few-shot methodology by comparing it to strong fine-tuning-based baseline models. Moreover, we construct and publicly release two new fact-checking datasets related to COVID-19.

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