10 citations · 22 across the 7 of their papers we have counts for
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cs.CL2022
An Overview on Controllable Text Generation via Variational Auto-Encoders
Haoqin Tu, Yitong Li
Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language. T…
cs.CL2022★ 10 cited
PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation
Haoqin Tu, Zhongliang Yang, Jinshuai Yang +2
Controllable text generation has taken a gigantic step forward these days. Yet existing methods are either constrained in a one-off pattern or not efficient enough for receiving mu…
cs.CL2022★ 4 cited
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
Haoqin Tu, Zhongliang Yang, Jinshuai Yang +1
Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time. Nevertheless, ex…