We evaluate our model against several strong baselines which apply VAE for text modelling (Bowman et al., 2016; <cite>Yang et al., 2017</cite>; Xu and Durrett, 2018) . Experimental results show that our HR-VAE model not only can effectively mitigate the latent variable collapse issue with a stable training process, but also can give better predictive performance than the baselines, as evidenced by both quantitative (e.g., negative log likelihood and perplexity) and qualitative evaluation.