Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Natural Language Generation in Dialogue using Lexicalized and
  Delexicalized Data   Natural language generation plays a critical role in spoken dialogue systems.
We present a new approach to natural language generation for task-oriented
dialogue using recurrent neural networks in an encoder-decoder framework. In
contrast to previous work, our model uses both lexicalized and delexicalized
components i.e. slot-value pairs for dialogue acts, with slots and
corresponding values aligned together. This allows our model to learn from all
available data including the slot-value pairing, rather than being restricted
to delexicalized slots. We show that this helps our model generate more natural
sentences with better grammar. We further improve our model's performance by
transferring weights learnt from a pretrained sentence auto-encoder. Human
evaluation of our best-performing model indicates that it generates sentences
which users find more appealing.

2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex