Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parameter Sharing Decoder Pair for Auto Composing   Auto Composing is an active and appealing research area in the past few
years, and lots of efforts have been put into inventing more robust models to
solve this problem. With the fast evolution of deep learning techniques, some
deep neural network-based language models are becoming dominant. Notably, the
transformer structure has been proven to be very efficient and promising in
modeling texts. However, the transformer-based language models usually contain
huge number of parameters and the size of the model is usually too large to put
in production for some storage limited applications. In this paper, we propose
a parameter sharing decoder pair (PSDP), which reduces the number of parameters
dramatically and at the same time maintains the capability of generating
understandable and reasonable compositions. Works created by the proposed model
are presented to demonstrate the effectiveness of the model.

31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
2
The categories of this paper are: math