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To simultaneously capture syntax and global semantics from a text corpus, wepropose a new larger-context recurrent neural network (RNN) based languagemodel, which extracts recurrent hierarchical semantic structure via a dynamicdeep topic model to guide natural language generation. Moving beyond aconventional RNN-based language model that ignores long-range word dependenciesand sentence order, the proposed model captures not only intra-sentence worddependencies, but also temporal transitions between sentences andinter-sentence topic dependencies. For inference, we develop a hybrid ofstochastic-gradient Markov chain Monte Carlo and recurrent autoencodingvariational Bayes. Experimental results on a variety of real-world text corporademonstrate that the proposed model not only outperforms larger-contextRNN-based language models, but also learns interpretable recurrent multilayertopics and generates diverse sentences and paragraphs that are syntacticallycorrect and semantically coherent.

Recurrent Hierarchical Topic-Guided RNN for Language Generation