Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Iterative Recursive Attention Model for Interpretable Sequence
  Classification   Natural language processing has greatly benefited from the introduction of
the attention mechanism. However, standard attention models are of limited
interpretability for tasks that involve a series of inference steps. We
describe an iterative recursive attention model, which constructs incremental
representations of input data through reusing results of previously computed
queries. We train our model on sentiment classification datasets and
demonstrate its capacity to identify and combine different aspects of the input
in an easily interpretable manner, while obtaining performance close to the
state of the art.

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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: eess
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th