Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Generate Questions with Adaptive Copying Neural Networks   Automatic question generation is an important problem in natural language
processing. In this paper we propose a novel adaptive copying recurrent neural
network model to tackle the problem of question generation from sentences and
paragraphs. The proposed model adds a copying mechanism component onto a
bidirectional LSTM architecture to generate more suitable questions adaptively
from the input data. Our experimental results show the proposed model can
outperform the state-of-the-art question generation methods in terms of BLEU
and ROUGE evaluation scores.

32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
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The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas