论文标题
通过BERT进行虚假新闻检测的分类预测背后的更好推理
Better Reasoning Behind Classification Predictions with BERT for Fake News Detection
论文作者
论文摘要
假新闻检测已成为解决的主要任务，因为近年来互联网上越来越多的假新闻。尽管已经基于统计学习方法提出了许多分类模型，但分类性能背后的推理可能还不够。在自我监督的学习研究中，有人强调说，代表质量（嵌入）空间很重要，直接影响下游任务绩效。在这项研究中，在真实和假新闻数据集中的不同类别的线性可分离性方面，在视觉和分析上分析了表示空间的质量。为了进一步为分类模型增加解释性，提出了类激活映射（CAM）的修改。修改后的CAM为每个单词令牌提供了CAM分数，其中词令牌上的CAM分数表示对该单词令牌的关注程度以进行预测。最后，结果表明，以可学习的线性层顶上的幼稚BERT模型足以在与CAM兼容的同时实现稳健的性能。
Fake news detection has become a major task to solve as there has been an increasing number of fake news on the internet in recent years. Although many classification models have been proposed based on statistical learning methods showing good results, reasoning behind the classification performances may not be enough. In the self-supervised learning studies, it has been highlighted that a quality of representation (embedding) space matters and directly affects a downstream task performance. In this study, a quality of the representation space is analyzed visually and analytically in terms of linear separability for different classes on a real and fake news dataset. To further add interpretability to a classification model, a modification of Class Activation Mapping (CAM) is proposed. The modified CAM provides a CAM score for each word token, where the CAM score on a word token denotes a level of focus on that word token to make the prediction. Finally, it is shown that the naive BERT model topped with a learnable linear layer is enough to achieve robust performance while being compatible with CAM.
