We evaluate our model against several strong baselines which apply VAE for text modelling <cite>(Bowman et al., 2016</cite>; Yang et al., 2017; Xu and Durrett, 2018) . We conducted experiments based on two public benchmark datasets, namely, the Penn Treebank dataset (Marcus and Marcinkiewicz, 1993) and the end-to-end (E2E) text generation dataset (Novikova et al., 2017) . 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.