Deep Latent-Variable Models for Text Generation

• URL: http://arxiv.org/abs/2203.02055v1
• Date: Thu, 3 Mar 2022 23:06:39 GMT
• Title: Deep Latent-Variable Models for Text Generation
• Authors: Xiaoyu Shen
• Abstract summary: Deep neural network-based end-to-end architectures have been widely adopted. End-to-end approach conflates all sub-modules, which used to be designed by complex handcrafted rules, into a holistic encode-decode architecture. This dissertation presents how deep latent-variable models can improve over the standard encoder-decoder model for text generation.
• Score: 7.119436003155924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text generation aims to produce human-like natural language output for down-stream tasks. It covers a wide range of applications like machine translation, document summarization, dialogue generation and so on. Recently deep neural network-based end-to-end architectures have been widely adopted. The end-to-end approach conflates all sub-modules, which used to be designed by complex handcrafted rules, into a holistic encode-decode architecture. Given enough training data, it is able to achieve state-of-the-art performance yet avoiding the need of language/domain-dependent knowledge. Nonetheless, deep learning models are known to be extremely data-hungry, and text generated from them usually suffer from low diversity, interpretability and controllability. As a result, it is difficult to trust the output from them in real-life applications. Deep latent-variable models, by specifying the probabilistic distribution over an intermediate latent process, provide a potential way of addressing these problems while maintaining the expressive power of deep neural networks. This dissertation presents how deep latent-variable models can improve over the standard encoder-decoder model for text generation.

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