  We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model
that assumes the generation of each word within a sentence to depend on both
the topic of the sentence and the whole history of its preceding words in the
sentence. Different from conventional topic models that largely ignore the
sequential order of words or their topic coherence, SLRTM gives full
characterization to them by using a Recurrent Neural Networks (RNN) based
framework. Experimental results have shown that SLRTM outperforms several
strong baselines on various tasks. Furthermore, SLRTM can automatically
generate sentences given a topic (i.e., topics to sentences), which is a key
technology for real world applications such as personalized short text
conversation.
