一种联合预测和解释陈述真实性的神经模型

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Erik Brand, Kevin Roitero, Michael Soprano, A. Rahimi, Gianluca Demartini
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引用次数: 4

摘要

自动事实核查(AFC)系统的存在是为了打击虚假信息,然而,它们的复杂性通常使它们对最终用户不透明,从而难以培养对系统的信任。在本文中,我们介绍了E-BART模型,希望在这方面取得进展。E-BART能够为索赔提供准确性预测,并共同为该决定生成人类可读的解释。我们证明了E-BART在e-FEVER和e-SNLI任务上与最先进的技术具有竞争力。此外,我们通过证明(1)生成解释不会显著阻碍模型在准确性预测的主要任务中表现良好,以及(2)预测的准确性和解释在联合生成时比单独生成时更具有内部一致性来验证联合预测架构。我们还校准了E-BART模型,允许最终模型的输出被正确地解释为正确性的置信度。最后,我们还对生成的解释的影响进行了广泛的人类评估,并观察到:解释提高了人类发现错误信息的能力,使人们对主张更加怀疑,E-BART生成的解释与基础事实解释具有竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Neural Model to Jointly Predict and Explain Truthfulness of Statements
Automated fact-checking (AFC) systems exist to combat disinformation, however, their complexity usually makes them opaque to the end-user, making it difficult to foster trust in the system. In this article, we introduce the E-BART model with the hope of making progress on this front. E-BART is able to provide a veracity prediction for a claim and jointly generate a human-readable explanation for this decision. We show that E-BART is competitive with the state-of-the-art on the e-FEVER and e-SNLI tasks. In addition, we validate the joint-prediction architecture by showing (1) that generating explanations does not significantly impede the model from performing well in its main task of veracity prediction, and (2) that predicted veracity and explanations are more internally coherent when generated jointly than separately. We also calibrate the E-BART model, allowing the output of the final model to be correctly interpreted as the confidence of correctness. Finally, we also conduct an extensive human evaluation on the impact of generated explanations and observe that: Explanations increase human ability to spot misinformation and make people more skeptical about claims, and explanations generated by E-BART are competitive with ground truth explanations.
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来源期刊
ACM Journal of Data and Information Quality
ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.10
自引率
4.80%
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0
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