The prevalence of half-truths, which are statements containing some truth but
that are ultimately deceptive, has risen with the increasing use of the
internet. To help combat this problem, we have created a comprehensive pipeline
consisting of a half-truth detection model and a claim editing model. Our
approach utilizes the T5 model for controlled claim editing; "controlled" here
means precise adjustments to select parts of a claim. Our methodology achieves
an average BLEU score of 0.88 (on a scale of 0-1) and a disinfo-debunk score of
85% on edited claims. Significantly, our T5-based approach outperforms other
Language Models such as GPT2, RoBERTa, PEGASUS, and Tailor, with average
improvements of 82%, 57%, 42%, and 23% in disinfo-debunk scores, respectively.
By extending the LIAR PLUS dataset, we achieve an F1 score of 82% for the
half-truth detection model, setting a new benchmark in the field. While
previous attempts have been made at half-truth detection, our approach is, to
the best of our knowledge, the first to attempt to debunk half-truths.

我们创建了一个包含半真实检测模型和声明编辑模型的综合流程，通过使用 T5 模型进行闭环控制的声明编辑来帮助解决互联网上半真实言论的问题。我们的方法在编辑后的声明上实现了平均 BLEU 得分为 0.88，并且 85% 的虚假揭穿得分。重要的是，相比于 GPT2、RoBERTa、PEGASUS 和 Tailor 等其他语言模型，我们基于 T5 的方法在虚假揭穿得分上平均提升了 82%、57%、42% 和 23%。通过扩展 LIAR PLUS 数据集，我们的半真实检测模型达到了 82% 的 F1 得分，创造了领域的新纪录。虽然以前有过半真实检测的尝试，但我们的方法是我们所知道的第一个试图揭穿半真实言论的。