Explainable and efficient knowledge acquisition from text

In a world where almost everything seems to come with a tl; dr, how do we make effective use of the large amount of knowledge that surrounds us and is growing every day? This dissertation focuses on addressing this question for the growing amount of knowledge that is encoded in the form of text with the help of natural language processing (NLP) systems. At a high level, it attempts to tackle two distinct problems: how to enable NLP systems to handle our complex information needs by enabling them to perform complex reasoning, and how to communicate efficiently and ask useful questions in a conversation when the request cannot be stated completely in the form of a single question. This dissertation presents several distinct approaches to tackle these problems. As these approaches are designed to solve relatively complex reasoning problems on our behalf, it is important to build trust between the user and the system to make sure the system is not just arriving at the right answers, but also doing so for the right reasons. Therefore, all of the approaches presented in this dissertation are also aimed at making the NLP systems involved more explainable for human understanding, and sometimes more controllable in their behavior through the same mechanism for explanation. Specifically, I first present my work on making use of linguistic information to aid the extraction of knowledge bases from textual data. Here, linguistically-motivated techniques combined with neural networks results in a new state of the art on knowledge extraction from text, which enables robust complex reasoning with this knowledge. Then, I move on to describe how we can complement knowledge-based approaches to question answering by extending it into a schema-less text-based setting. Here, we collect one of the first large-scale datasets for open-domain text-based multi-hop question answering, and then I present a system that iteratively retrieves supporting documents from a large collection of text to answer these text-based complex questions. Finally, as we improve NLP systems' capability of performing complex reasoning to answer questions, I note that it is important that they also accommodate our information needs that are sometimes too complex or under-defined to express in a single complex question. To this end, I present how to train NLP systems to ask inquisitive questions to gather knowledge in the face of information asymmetry. This not only helps them gather important information to help us resolve our information needs, but also allows systems to reason about how we will gather information in an interaction, and present textual knowledge in a more efficient manner to reduce unnecessary confusion. By defining the informativeness of inquisitive questions and optimizing for information gathering, the resulting system generates curiosity-driven questions to help the system learn more about previously unknown knowledge in a conversation. By demonstrating and examining these systems, I also hope to show how designing NLP systems for explainability can help us attain various notions of efficiency necessitated by the need to process and present textual knowledge from large collections of text we wish to make use of

Advisor: Manning Christopher D.
Jurafsky Dan
Liang Percy