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Title:HiBi: A hierarchical bigram model for associative learning
Author(s):Wang, Xiaoyan
Advisor(s):Zhai, Chengxiang
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Natural language processing
Cognitive models
Abstract:There has been a shift of attention in the AI research where people gradually abandon traditional statistical models in favor of deep neural architectures. While effective in learning input-output mappings from two arbitrary distributions, the complex nature of neural models makes them hard to interpret. In this thesis, we introduce a more interpretable hierarchical bigram (HiBi) model, which is extended based on the simple bigram language model. It contains a few components inspired by theories of human cognition, and has been shown through experiments to be effective in learning meaningful representation from sequential inputs without any labeling. We hope that HiBi could be a starting point to develop more complex cognitive models that are both interpretable and effective for representation learning.
Issue Date:2020-05-11
Rights Information:Copyright 2020 Xiaoyan Wang
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05

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