Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discrete Structural Planning for Neural Machine Translation   Structural planning is important for producing long sentences, which is a
missing part in current language generation models. In this work, we add a
planning phase in neural machine translation to control the coarse structure of
output sentences. The model first generates some planner codes, then predicts
real output words conditioned on them. The codes are learned to capture the
coarse structure of the target sentence. In order to obtain the codes, we
design an end-to-end neural network with a discretization bottleneck, which
predicts the simplified part-of-speech tags of target sentences. Experiments
show that the translation performance are generally improved by planning ahead.
We also find that translations with different structures can be obtained by
manipulating the planner codes.

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The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
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The categories of this paper are: nlin
21
The categories of this paper are: adap-org
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The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex