Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sentence Level Recurrent Topic Model: Letting Topics Speak for
  Themselves   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.

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The categories of this paper are: q-alg
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: comp-gas