Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ordered Memory   Stack-augmented recurrent neural networks (RNNs) have been of interest to the
deep learning community for some time. However, the difficulty of training
memory models remains a problem obstructing the widespread use of such models.
In this paper, we propose the Ordered Memory architecture. Inspired by Ordered
Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and
use its cumulative probability to control the writing and erasing operation of
the memory. We also introduce a new Gated Recursive Cell to compose lower-level
representations into higher-level representation. We demonstrate that our model
achieves strong performance on the logical inference task (Bowman et al.,
2015)and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the
model to retrieve the induced tree structure, and find that these induced
structures align with the ground truth. Finally, we evaluate our model on the
Stanford SentimentTreebank tasks (Socher et al., 2013), and find that it
performs comparatively with the state-of-the-art methods in the literature.

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The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio