Paper:
To simultaneously capture syntax and global semantics from a text corpus, we propose a new larger-context recurrent neural network (RNN) based language model, which extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation. Moving beyond a conventional RNN based language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependences. For inference, we develop a hybrid of stochastic-gradient MCMC and recurrent autoencoding variational Bayes. Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms state-ofthe-art larger-context RNN-based language models, but also learns interpretable recurrent multilayer topics and generates diverse sentences and paragraphs that are syntactically correct and semantically coherent.

Review:
[Additional review]
This paper proposes a technique to incorporate document-level topic model information into language models. 
While the underlying idea is interesting, my biggest issue is with the misleading assertions at the very beginning of the paper. In the second paragraph of Section 1, the paper claims that RNN-based LMs often make independence assumptions between sentences, hence why they develop a topic modelling approach to model document-level information. Some issues with this claim:
1. Pretty much every LM paper that evaluates on language modelling benchmark (PTB, WT-103, Wikitext-2) uses LSTMs/Transformers incorporate cross-sentential, document-level information as context, through a very simple approach of just concatenating all the sentences and adding a unique token to mark sentence boundaries.
2. Prior work has shown that LSTMs/Transformers with cross-sentential context can, and in fact do, make use of information from previous sentences.
a. Evidence 1: Khandelwal et al. (2018) showed that LSTMs memorise word orders from the past ~50 tokens, and retain semantic information from the past ~200 tokens; both of which extend far beyond the length of an average sentence, suggesting that information from the previous sentences is used in the predictions of the current sentence.
b. Evidence 2: Language models that operate on single sentences typically do worse than language models that take into account cross-sentential context, e.g. the language model of Kim et al. (2019) that operates on single sentences gets ~90 ppl. on PTB test set, while LSTMs that condition on multiple sentences get a much better ~50-something ppl. on Mikolov PTB.
Crucially, these prior works defeat the paper’s motivation of why it claims to need topic models in the first place (i.e. to model cross-sentential context), while just concatenating multiple sentences as context would do, and in fact has been done many times.
2. Prior work (mostly in Transformer-land) has come up with ways to make use of very long-range context, from Transformer-XL to the more recent compressive Transformer (https://openreview.net/forum?id=SylKikSYDH) that can condition on entire books. While these are done for Transformers, in principle one can also apply similar techniques to LSTMs.
3. While Transformer-XL has the potential to make use of word orders in the preceding sentences, it seems that this paper’s approach cannot do that, since they only take the bag-of-words from the preceding sentences. It thus seems that their bag-of-word approach is less expressive, and hence less powerful, than the simpler alternative of concatenating sentences.
4. The perplexity results (Table 1) are not done on very standard datasets (no PTB evaluation for instance). It is thus hard to evaluate the strength of the baseline models. In the paper's defense, it seems that they were following the experimental setup of Wang et al. (2019), but the paper should elaborate more on the choice of evaluation datasets.
5. The inference part is not particularly self-contained. The paper simply refers the TLASGR-MCMC method (which is an important part to make inference scalable) to prior work (Cong et al., 2017; Zhang et al., 2018), yet does not explain (even briefly) how the approach works, and how it can be combined with their recurrent topic model formulation.  
6. Evaluation of the induced topic hierarchy (Figure 4) is only done through qualitative samples, and the paper does not really explain how to pick the samples (i.e. possible cherry-picking). I am not very familiar with the topic modelling literature, but it would be nice if the induced hierarchy can be evaluated quantitatively.
References:
1. Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky. Sharp nearby, fuzzy far away. In Proc. of ACL 2018.
2. Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, and Gabor Melis. Unsupervised recurrent neural network grammars. In Proc. of NAACL 2019.
3. Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, and Lawrence Carin. Topic-guided variational autoencoders for text generation. In Proc. of NAACL 2019.
4. Yulai Cong, Bo Chen, Hongwei Liu, and Mingyuan Zhou. Deep latent Dirichlet allocation with topic-layer-adaptive stochastic gradient Riemannian MCMC. In Proc. of ICML 2017
5. Hao Zhang, Bo Chen, Dandan Guo, and Mingyuan Zhou. WHAI: Weibull hybrid autoencoding inference for deep topic modeling. In Proc. of ICLR 2018