AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling

• URL: http://arxiv.org/abs/2205.05862v1
• Date: Thu, 12 May 2022 03:22:07 GMT
• Title: AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
• Authors: Haoqin Tu, Zhongliang Yang, Jinshuai Yang, Siyu Zhang, Yongfeng Huang
• Abstract summary: Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving both representation learning and generation for natural language. Existing VAE-based language models either employ elementary RNNs, or fine-tunes two pre-trained language models (PLMs) for any downstream task, which requires huge energy consumption. In this paper, we introduce the first VAE framework empowered with adaptive GPT-2s (AdaVAE)
• Score: 33.18577107062907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving both representation learning and generation for natural language. However, existing VAE-based language models either employ elementary RNNs, which is not powerful to handle multi-tasks, or fine-tunes two pre-trained language models (PLMs) for any downstream task, which requires huge energy consumption. In this paper, we introduce the first VAE framework empowered with adaptive GPT-2s (AdaVAE). Different from mentioned systems, we unify both the encoder and decoder of VAE model using GPT-2s with adaptive parameter-efficient components. Experiments from multiple dimensions validate that AdaVAE is competent to better organize language in generation and representation modeling, even with less than $15\%$ additionally activated parameters during training. Our code is available at \url{https://github.com/ImKeTT/adavae}.

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