Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.

翻译：动态专题建模有助于确定非结构化文件临时收集时间的时空趋势。我们引入了一个新的不受监督的神经动态专题模型,名为经常性神经网络复制软体模型(RNNNSM),每次发现的专题都会影响随后时间步骤中的专题发现。我们使用具有时空重复连接的分布性估计符,明确模拟长期潜在时地依赖性的联合分布,以此说明文件的时间顺序。我们应用RNN-RSM至19年的NLP研究文章,我们证明与最新艺术专题模型相比,RNNRSM显示了更好的一般化、专题解释、演变和趋势。我们还采用了一个指标(称为SPAN),以量化动态专题模型在时间上反映专题演变的能力。