Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic
  Representations   Stance detection is an important component of understanding hidden influences
in everyday life. Since there are thousands of potential topics to take a
stance on, most with little to no training data, we focus on zero-shot stance
detection: classifying stance from no training examples. In this paper, we
present a new dataset for zero-shot stance detection that captures a wider
range of topics and lexical variation than in previous datasets. Additionally,
we propose a new model for stance detection that implicitly captures
relationships between topics using generalized topic representations and show
that this model improves performance on a number of challenging linguistic
phenomena.

26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas