Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Neural Networks with External Memory for Language
  Understanding   Recurrent Neural Networks (RNNs) have become increasingly popular for the
task of language understanding. In this task, a semantic tagger is deployed to
associate a semantic label to each word in an input sequence. The success of
RNN may be attributed to its ability to memorize long-term dependence that
relates the current-time semantic label prediction to the observations many
time instances away. However, the memory capacity of simple RNNs is limited
because of the gradient vanishing and exploding problem. We propose to use an
external memory to improve memorization capability of RNNs. We conducted
experiments on the ATIS dataset, and observed that the proposed model was able
to achieve the state-of-the-art results. We compare our proposed model with
alternative models and report analysis results that may provide insights for
future research.

12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics