Reasoning-Driven Question-Answering for Natural Language Understanding

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.

arXiv commentPhD Dissertation; Presented to Computer and Information Sciences department, at the University of Pennsylvania

References in corpus (13)

• Efficient Estimation of Word Representations in Vector Space
• From Frequency to Meaning: Vector Space Models of Semantics
• Teaching Machines to Read and Comprehend
• Bidirectional Attention Flow for Machine Comprehension
• Enhanced LSTM for Natural Language Inference
• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• The Use of Classifiers in Sequential Inference
• A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
• Question Answering via Integer Programming over Semi-Structured Knowledge
• Question Answering through Transfer Learning from Large Fine-grained Supervision Data
• IllinoisSL: A JAVA Library for Structured Prediction
• Evidence Sentence Extraction for Machine Reading Comprehension