论文标题
使用变压器解释可解释的言语欺骗检测
Explainable Verbal Deception Detection using Transformers
论文作者
论文摘要
人们经常面临潜在的欺骗性陈述（例如，假新闻，误导性产品评论或有关活动的谎言）。基于文本的自动欺骗检测只有很少的作品利用了深度学习方法的潜力。对深度学习方法的批评是它们缺乏解释性，阻止我们理解欺骗所涉及的潜在（语言）机制。但是，最近的进步使解释了此类模型的某些方面成为可能。本文提出并评估了六个深度学习模型，包括Bert（和Roberta）的组合，多头注意力，共同发作和变形金刚。为了了解模型如何做出决策，我们然后使用石灰检查模型的预测。然后，我们放大了词汇唯一性以及LIWC类别与结果类别的相关性（真实与欺骗性）。研究结果表明，我们的基于变压器的模型可以增强自动化欺骗检测性能（准确性+2.11％），并显示出与LIWC特征在真实和欺骗性陈述中使用有关的显着差异。
People are regularly confronted with potentially deceptive statements (e.g., fake news, misleading product reviews, or lies about activities). Only few works on automated text-based deception detection have exploited the potential of deep learning approaches. A critique of deep-learning methods is their lack of interpretability, preventing us from understanding the underlying (linguistic) mechanisms involved in deception. However, recent advancements have made it possible to explain some aspects of such models. This paper proposes and evaluates six deep-learning models, including combinations of BERT (and RoBERTa), MultiHead Attention, co-attentions, and transformers. To understand how the models reach their decisions, we then examine the model's predictions with LIME. We then zoom in on vocabulary uniqueness and the correlation of LIWC categories with the outcome class (truthful vs deceptive). The findings suggest that our transformer-based models can enhance automated deception detection performances (+2.11% in accuracy) and show significant differences pertinent to the usage of LIWC features in truthful and deceptive statements.
