Reasoning-Driven Question-Answering for Natural Language Understanding

Autor/in	Khashabi, Daniel
Titel	Reasoning-Driven Question-Answering for Natural Language Understanding
Quelle	(2019), (213 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, University of Pennsylvania
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-0855-8287-2
Schlagwörter	Hochschulschrift; Dissertation; Natural Language Processing; Artificial Intelligence; Task Analysis; Questioning Techniques; Logical Thinking; Sentences; Ambiguity (Semantics); Computational Linguistics; Language Usage; Guidelines + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Natürliche Sprache; Künstliche Intelligenz; Aufgabenanalyse; Befragungstechnik; Fragetechnik; Sentence analysis; Satzanalyse; Linguistics; Computerlinguistik; Sprachgebrauch; Richtlinien
Abstract	"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. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Update	2024/1/01