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Reasoning-Driven Question-Answering for Natural Language Understanding / Daniel Khashabi.
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Khashabi, Daniel, author.
- Language:
- English
- Subjects (All):
- Computer science.
- Artificial intelligence.
- Information science.
- Computer and information science--Penn dissertations.
- Penn dissertations--Computer and information science.
- Local Subjects:
- Computer science.
- Artificial intelligence.
- Information science.
- Computer and information science--Penn dissertations.
- Penn dissertations--Computer and information science.
- Genre:
- Academic theses.
- Physical Description:
- 1 online resource (213 pages)
- Contained In:
- Dissertations Abstracts International 81-02A.
- Place of Publication:
- [Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2019.
- Language Note:
- English
- System Details:
- Mode of access: World Wide Web.
- text file
- Summary:
- Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems.In the final part, we present the first formal framework for multi-step reasoning algorithms,in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.
- Notes:
- Source: Dissertations Abstracts International, Volume: 81-02, Section: A.
- Advisors: Roth, Dan; Committee members: Mitch Marcus; Zack Ives; Chris Callison-Burch; Ashish Sabharwal.
- Department: Computer and Information Science.
- Ph.D. University of Pennsylvania 2019.
- Local Notes:
- School code: 0175
- ISBN:
- 9781085582872
- Access Restriction:
- Restricted for use by site license.
- This item is not available from ProQuest Dissertations & Theses.
- This item must not be sold to any third party vendors.
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