Deep generative models for natural language processing

Thesis

Deep generative models are essential to Natural Language Processing (NLP) due to their outstanding ability to use unlabelled data, to incorporate abundant linguistic features, and to learn interpretable dependencies among data. As the structure becomes deeper and more complex, having an effective and efficient inference method becomes increasingly important. In this thesis, neural variational inference is applied to carry out inference for deep generative models. While traditional variatio...

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Authors: Miao, Y

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Keywords: Neural Variational Inference; Natural Language Processing; Dialogue System; Topic Model; Deep Learning; Variational Autoencoder; Summarisation; Deep Generative Models