On End-to-end Automatic Fact-checking Systems
Name
Fang-wfang-SM-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
2.13 MB
Format
Adobe PDF
Checksum (MD5)
12ee6d4f3923d8e6d4fe20dd2080e49f
Author(s)
Fang, Wei
Advisor(s)
Glass, James R.
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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