基于Transformers的可解释言语欺骗检测
Explainable Verbal Deception Detection using Transformers
摘要：
人们经常面对潜在的欺骗性陈述（例如，虚假新闻、误导性产品评论或关于活动的谎言）。只有少数基于文本的自动欺骗检测工作利用了深度学习方法的潜力。对深度学习方法的一个批评是它们缺乏可解释性，这阻止了我们理解欺骗所涉及的潜在（语言）机制。然而，最近的进展使人们有可能解释这些模型的某些方面。本文提出并评估了六种深度学习模型，包括BERT（和RoBERTa）、MultiHead Attention、co-attention和Transformers的组合。为了理解模型是如何做出决策的，我们用LIME来检验模型的预测。然后，我们放大词汇的独特性以及LIWC类别与结果类别（真实与欺骗）的相关性。研究结果表明，我们基于transformer的模型可以提高自动欺骗检测性能（准确率为+2.11%），并在真实和欺骗性陈述中显示出与LIWC特征使用相关的显著差异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
