{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139967"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139967","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"On End-to-end Automatic Fact-checking Systems","abstract":"The emergence of social media has aided the spread of nonfactual information across the internet, and organizations are combating disinformation by performing manual fact-checking. Due to the massive amount of online information, the automation of this process has recently gained great interest. Previous works have formulated several automatic fact-checking tasks, and explored machine learning and natural language processing approaches to the problems. In this thesis we follow this line of work, aim to build a fully-working automatic fact-checking system, and study methods for improving its fact-checking abilities. First, we introduce an end-to-end automatic fact-checking framework that integrates multiple previously studied subtasks to predict the factuality of given claims while providing supporting evidence. Next we explore the use of multi-task learning for improving factuality predictions. Finally, we devise methods for extracting temporal structure from news documents to aid the fact-checking process.","abstract_html":"The emergence of social media has aided the spread of nonfactual information across the internet, and organizations are combating disinformation by performing manual fact-checking. Due to the massive amount of online information, the automation of this process has recently gained great interest. Previous works have formulated several automatic fact-checking tasks, and explored machine learning and natural language processing approaches to the problems. In this thesis we follow this line of work, aim to build a fully-working automatic fact-checking system, and study methods for improving its fact-checking abilities. First, we introduce an end-to-end automatic fact-checking framework that integrates multiple previously studied subtasks to predict the factuality of given claims while providing supporting evidence. Next we explore the use of multi-task learning for improving factuality predictions. Finally, we devise methods for extracting temporal structure from news documents to aid the fact-checking process.","abstract_has_math":false,"creators":["Fang, Wei"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Due to the massive amount of online information, the automation of this process has recently gained great interest. Previous works have formulated several automatic fact-checking tasks, and explored machine learning and natural language processing approaches to the problems. In this thesis we follow this line of work, aim to build a fully-working automatic fact-checking system, and study methods for improving its fact-checking abilities. First, we introduce an end-to-end automatic fact-checking framework that integrates multiple previously studied subtasks to predict the factuality of given claims while providing supporting evidence. Next we explore the use of multi-task learning for improving factuality predictions. 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Previous works have formulated several automatic fact-checking tasks, and explored machine learning and natural language processing approaches to the problems. In this thesis we follow this line of work, aim to build a fully-working automatic fact-checking system, and study methods for improving its fact-checking abilities. First, we introduce an end-to-end automatic fact-checking framework that integrates multiple previously studied subtasks to predict the factuality of given claims while providing supporting evidence. Next we explore the use of multi-task learning for improving factuality predictions. 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