Neural reading comprehension and beyond

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Teaching machines to understand human language documents is one of the most elusive and long-standing challenges in Artificial Intelligence. This thesis tackles the problem of reading comprehension: how to build computer systems to read a passage of text and answer comprehension questions. On the one hand, we think that reading comprehension is an important task for evaluating how well computer systems understand human language. On the other hand, if we can build high-performing reading comprehension systems, they would be a crucial technology for applications such as question answering and dialogue systems. In this thesis, we focus on neural reading comprehension: a class of reading comprehension models built on top of deep neural networks. Compared to traditional sparse, hand-designed feature-based models, these end-to-end neural models have proven to be more effective in learning rich linguistic phenomena and improved performance on all the modern reading comprehension benchmarks by a large margin. This thesis consists of two parts. In the first part, we aim to cover the essence of neural reading comprehension and present our efforts at building effective neural reading comprehension models, and more importantly, understanding what neural reading comprehension models have actually learned, and what depth of language understanding is needed to solve current tasks. We also summarize recent advances and discuss future directions and open questions in this field. In the second part of this thesis, we investigate how we can build practical applications based on the recent success of neural reading comprehension. In particular, we pioneered two new research directions: 1) how we can combine information retrieval techniques with neural reading comprehension to tackle large-scale open-domain question answering; and 2) how we can build conversational question answering systems from current single-turn, span-based reading comprehension models. We implemented these ideas in the DrQA and CoQA projects and we demonstrate the effectiveness of these approaches. We believe that they hold great promise for future language technologies.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2018; ©2018
Publication date 2018; 2018
Issuance monographic
Language English


Author Chen, Danqi
Degree supervisor Manning, Christopher D
Thesis advisor Manning, Christopher D
Thesis advisor Jurafsky, Dan, 1962-
Thesis advisor Liang, Percy
Thesis advisor Zettlemoyer, Luke S, 1978-
Degree committee member Jurafsky, Dan, 1962-
Degree committee member Liang, Percy
Degree committee member Zettlemoyer, Luke S, 1978-
Associated with Stanford University, Computer Science Department.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Danqi Chen.
Note Submitted to the Computer Science Department.
Thesis Thesis Ph.D. Stanford University 2018.
Location electronic resource

Access conditions

© 2018 by Danqi Chen
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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