III: Small: Collaborative Research: Towards End-to-End Computer-Assisted Fact-Checking

该项目将开发ClaimBuster，这是一个用于计算机辅助事实核查的端到端系统。该系统将监控现场演讲、社交媒体和新闻，以捕捉事实主张，通过专业人士精心策划的事实核查存储库检测匹配，并将匹配即时发送给读者和观众。对于以前没有检查过的各种类型的新索赔，ClaimBuster会自动将它们与知识数据库进行核对，并报告它们是否属实。对于必须将人类带入循环的新索赔，该系统将提供算法和计算工具，以帮助非专业人员和专业人士理解和审查索赔。在完成拟议的工作后，ClaimBuster将成为有史以来第一个用于广泛事实索赔的自动事实核查系统。它的用途将扩大到核实各种类型的叙事、语篇和文件中的主张，如体育新闻、法律文件和金融报道。它可以使大量潜在用户受益，其中包括消费者、出版商、企业竞争对手和法律专业人士等。它通过提高信息的准确性和透明度直接使消费者受益。它帮助新闻机构加快事实核查过程，并确保其新闻报道的准确性。企业可以使用ClaimBuster来识别竞争对手和自己的报告和新闻稿中的虚假内容。它还帮助律师等专业人士核实文件。ClaimBuster将使用数据库查询、数据挖掘和自然语言处理技术来协助事实核查。本项目的具体研究任务如下。(1)研究如何模拟事实索赔并产生其内部表述。为此，团队将在不同的领域中创建索赔模板的分类法，根据分类法对索赔进行分类，并通过对索赔的文本形式进行语义解析来生成内部表示。这种特定领域的建模和索赔的内部表示将使系统的其他组件能够采用新的方法和系统的、连贯的解决方案。(2)在算法事实核查方面，他们将设计新的方法，将索赔转化为结构化查询、关键字查询和自然语言问题。对一般和领域特定数据库和知识图的这些查询结果将与声明本身中嵌入的答案进行比较，以验证声明是否属实。(3)通过将索赔视为参数化查询，他们将开发基于扰动分析的方法，以找到索赔的反驳论点，并从数据集中找到“有趣的”事实。这些结果将帮助ClaimBuster识别“挑剔”的说法--那些正确但具有误导性的说法。

This project will develop ClaimBuster, an end-to-end system for computer-assisted fact-checking. This system will monitor live discourses, social media, and news to catch factual claims, detect matches with a curated repository of fact-checks from professionals, and deliver the matches instantly to readers and viewers. For various types of new claims not checked before, ClaimBuster will automatically check them against knowledge databases and report if they are truthful. For novel claims where humans must be brought into the loop, the system will provide algorithmic and computational tools to assist laypersons and professionals in understanding and vetting the claims. ClaimBuster, upon completion of the proposed work, is positioned to become the first-ever automated fact-checking system for use on a broad spectrum of factual claims. Its use will be expanded to verify claims in various types of narratives, discourses and documents such as sports news, legal documents, and financial reports. It can benefit a large base of potential users including consumers, publishers, corporate competitors, and legal professionals, among others. It directly benefits consumers by improving information accuracy and transparency. It helps news organizations speed their fact-checking process and also ensure the accuracy of their own news stories. Businesses can use ClaimBuster to identify falsehoods in their competitors' and their own reports and press releases. It also assists professionals such as lawyers in verifying documents.ClaimBuster will use database query, data mining, and natural language processing techniques to aid fact-checking. The detailed research tasks in this project will be as follows. (1) Investigate how to model factual claims and produce their internal representations. For this, the team will create taxonomies of claim templates in different domains, categorize claims based on the taxonomies, and generate internal representations through semantic parsing of the claims' textual forms. Such domain-specific modeling and internal representation of claims will enable novel methods and systematic, coherent solutions for other components of the system. (2) For algorithmic fact-checking, they will devise novel methods for translating claims into structured queries, keyword queries and natural language questions. Results of these queries over general and domain-specific databases and knowledge graphs will be compared with the answers embedded in the claims themselves, to verify if the claims check out. (3) By viewing claims as parameterized queries, they will develop methods based on perturbation analysis to find counter-arguments to claims and to find "interesting" factlets from datasets. These results will help ClaimBuster in identifying "cherry-picking" claims -- claims that are correct but misleading.