Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linguistic Versus Latent Relations for Modeling Coherent Flow in
  Paragraphs   Generating a long, coherent text such as a paragraph requires a high-level
control of different levels of relations between sentences (e.g., tense,
coreference). We call such a logical connection between sentences as a
(paragraph) flow. In order to produce a coherent flow of text, we explore two
forms of intersentential relations in a paragraph: one is a human-created
linguistical relation that forms a structure (e.g., discourse tree) and the
other is a relation from latent representation learned from the sentences
themselves. Our two proposed models incorporate each form of relations into
document-level language models: the former is a supervised model that jointly
learns a language model as well as discourse relation prediction, and the
latter is an unsupervised model that is hierarchically conditioned by a
recurrent neural network (RNN) over the latent information. Our proposed models
with both forms of relations outperform the baselines in partially conditioned
paragraph generation task. Our codes and data are publicly available.

25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con