Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Gated Self-attention Memory Network for Answer Selection   Answer selection is an important research problem, with applications in many
areas. Previous deep learning based approaches for the task mainly adopt the
Compare-Aggregate architecture that performs word-level comparison followed by
aggregation. In this work, we take a departure from the popular
Compare-Aggregate architecture, and instead, propose a new gated self-attention
memory network for the task. Combined with a simple transfer learning technique
from a large-scale online corpus, our model outperforms previous methods by a
large margin, achieving new state-of-the-art results on two standard answer
selection datasets: TrecQA and WikiQA.

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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math