FactCatch:以最小的用户工作量进行增量付费事实检查

T. Nguyen, M. Weidlich, Hongzhi Yin, Bolong Zheng, Q. Nguyen, Quoc Viet Hung Nguyen
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引用次数: 12

摘要

Web的开放特性使用户能够在没有身份验证的情况下生成和传播任何内容,这已经被利用,通过数百万个在线文档传播了数千个未经验证的声明。因此,可信知识库的维护必须依赖于通过可信度评估构建可信事实集的事实检查。由于缺乏真实信息和语言的模糊性,事实检查无法在不影响准确性的情况下以纯粹自动化的方式完成。然而,最先进的事实检查服务主要依赖于人工验证,这是昂贵的、缓慢的和不透明的。本文介绍了FactCatch,这是一个指导用户事实检查的人在循环系统,旨在最大限度地减少投入的努力。它支持增量质量评估、错误缓解和高质量事实数据库的随用随付实例化。
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FactCatch: Incremental Pay-as-You-Go Fact Checking with Minimal User Effort
The open nature of the Web enables users to produce and propagate any content without authentication, which has been exploited to spread thousands of unverified claims via millions of online documents. Maintenance of credible knowledge bases thus has to rely on fact checking that constructs a trusted set of facts through credibility assessment. Due to an inherent lack of ground truth information and language ambiguity, fact checking cannot be done in a purely automated manner without compromising accuracy. However, state-of-the-art fact checking services, rely mostly on human validation, which is costly, slow, and non-transparent. This paper presents FactCatch, a human-in-the-loop system to guide users in fact checking that aims at minimisation of the invested effort. It supports incremental quality estimation, mistake mitigation, and pay-as-you-go instantiation of a high-quality fact database.
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