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Methods and Challenges in Inference Across Textual Sources Sihao Chen
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Chen, Sihao, author.
- Language:
- English
- Subjects (All):
- Computer science.
- Information technology.
- 0984.
- 0800.
- 0489.
- Local Subjects:
- Computer science.
- Information technology.
- 0984.
- 0800.
- 0489.
- Physical Description:
- 1 electronic resource (152 pages)
- Contained In:
- Dissertations Abstracts International 86-07B
- Place of Publication:
- Ann Arbor : ProQuest Dissertations and Theses, 2024
- Language Note:
- English
- Summary:
- Information technologies, such as search engines, or the more recent generative AI models, have enabled and democratized access to a vast array of content from diverse sources for internet users. However, discerning what is trustworthy across the sheer volume of information has become a challenge for us.The goal of this dissertation is to develop natural language processing (NLP) techniques to facilitate effective, efficient comparison and validation of information across multiple sources. In NLP, The problem of comparing information across sources closely resembles the task of natural language inference (NLI), where a system is expected to classify whether a hypothesis can be inferred from a premise. In this thesis, I focus on addressing three key problems in the standard practices in NLI. First, for open-ended questions or claims with many possible answers, I argue that a system should discover supporting and opposing evidence from a diverse set of perspectives. Next, to facilitate a more fine-grained level comparison of information across sources, I propose a representation learning framework where text semantics are represented by propositions. Lastly, I show that current NLI datasets and models suffer from the assumption that the claim and evidence can always be interpreted in the same context, which can negatively impact NLI models' applicability as a fact verification model in real-world settings
- Notes:
- Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
- Advisors: Roth, Dan Committee members: Callison-Burch, Chris; Yatskar, Mark; Ungar, Lyle; Durrett, Greg
- Ph.D. University of Pennsylvania 2024
- Local Notes:
- School code: 0175
- ISBN:
- 9798302184276
- Access Restriction:
- Restricted for use by site license
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