Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Urdu Caption Generation using Attention based LSTM   Recent advancements in deep learning have created many opportunities to solve
real-world problems that remained unsolved for more than a decade. Automatic
caption generation is a major research field, and the research community has
done a lot of work on it in most common languages like English. Urdu is the
national language of Pakistan and also much spoken and understood in the
sub-continent region of Pakistan-India, and yet no work has been done for Urdu
language caption generation. Our research aims to fill this gap by developing
an attention-based deep learning model using techniques of sequence modeling
specialized for the Urdu language. We have prepared a dataset in the Urdu
language by translating a subset of the "Flickr8k" dataset containing 700 'man'
images. We evaluate our proposed technique on this dataset and show that it can
achieve a BLEU score of 0.83 in the Urdu language. We improve on the previous
state-of-the-art by using better CNN architectures and optimization techniques.
Furthermore, we provide a discussion on how the generated captions can be made
correct grammar-wise.

20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
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The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th