论文标题
连接事实验证与虚假新闻检测之间的点
Connecting the Dots Between Fact Verification and Fake News Detection
论文作者
论文摘要
在过去的两年中，随着预训练的语言模型（如伯特（Bert））以及发烧等大型数据集的发布，事实验证模型在过去两年中取得了快速的进步。但是，假新闻检测的挑战性问题并没有从事实验证模型的改进中受益，这与假新闻检测密切相关。在本文中，我们提出了一种简单而有效的方法，以将事实验证与虚假新闻检测之间的点联系起来。我们的方法首先采用了在新闻文献中预先培训的文本摘要模型，以简短的主张总结长新闻文章。然后，我们使用在发烧数据集上预先训练的事实验证模型来检测输入新闻文章是真实的还是假的。我们的方法利用了事实验证模型的最新成功，并实现了零射的假新闻检测，从而减轻了对大规模培训数据的需求来培训伪造新闻检测模型。用于假新闻检测基准数据集Fakenewsnet的实验结果证明了我们提出的方法的有效性。
Fact verification models have enjoyed a fast advancement in the last two years with the development of pre-trained language models like BERT and the release of large scale datasets such as FEVER. However, the challenging problem of fake news detection has not benefited from the improvement of fact verification models, which is closely related to fake news detection. In this paper, we propose a simple yet effective approach to connect the dots between fact verification and fake news detection. Our approach first employs a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. Then we use a fact verification model pre-trained on the FEVER dataset to detect whether the input news article is real or fake. Our approach makes use of the recent success of fact verification models and enables zero-shot fake news detection, alleviating the need of large-scale training data to train fake news detection models. Experimental results on FakenewsNet, a benchmark dataset for fake news detection, demonstrate the effectiveness of our proposed approach.
