Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Rule Extraction from Long Short Term Memory Networks   Although deep learning models have proven effective at solving problems in
natural language processing, the mechanism by which they come to their
conclusions is often unclear. As a result, these models are generally treated
as black boxes, yielding no insight of the underlying learned patterns. In this
paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new
approach for tracking the importance of a given input to the LSTM for a given
output. By identifying consistently important patterns of words, we are able to
distill state of the art LSTMs on sentiment analysis and question answering
into a set of representative phrases. This representation is then
quantitatively validated by using the extracted phrases to construct a simple,
rule-based classifier which approximates the output of the LSTM.

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The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: eess