Recurrent Hierarchical Topic-Guided RNN for Language Generation

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou


<p>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 Markov chain Monte Carlo and recurrent autoencoding variational Bayes. Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms 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.</p>