Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rationale-Augmented Convolutional Neural Networks for Text
  Classification   We present a new Convolutional Neural Network (CNN) model for text
classification that jointly exploits labels on documents and their component
sentences. Specifically, we consider scenarios in which annotators explicitly
mark sentences (or snippets) that support their overall document
categorization, i.e., they provide rationales. Our model exploits such
supervision via a hierarchical approach in which each document is represented
by a linear combination of the vector representations of its component
sentences. We propose a sentence-level convolutional model that estimates the
probability that a given sentence is a rationale, and we then scale the
contribution of each sentence to the aggregate document representation in
proportion to these estimates. Experiments on five classification datasets that
have document labels and associated rationales demonstrate that our approach
consistently outperforms strong baselines. Moreover, our model naturally
provides explanations for its predictions.

22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga