Monash_PhD_thesis_revised_Yuncheng_Hua.pdf (4.32 MB)

Complex Question Answering over Large-scale Knowledge Bases

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thesis
posted on 29.07.2021, 07:00 by YUNCHENG HUA
In this research study, we propose an effective framework for complex question answering over large-scale knowledge bases. We employ encoder-decoder neural networks, deep learning, reinforcement learning, meta-learning, few-shot learning algorithms, and other effective techniques to construct our question-answering framework to address the challenges existing in previous state-of-the-art question answering systems.  Empirical studies over large-scale CQA datasets not only indicate that our proposed approach is effective as it outperforms state-of-the-art methods significantly and also shed light on the role that specific components play in the question-answering task.

History

Campus location

Australia

Principal supervisor

Yuan-fang Li

Year of Award

2021

Department, School or Centre

Clayton School of IT

Additional Institution or Organisation

Faculty of Information Technology

Course

Doctor of Philosophy (Joint PhD with Southeast University - International)

Degree Type

DOCTORATE