Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Abusive Language Detection in Heterogeneous Contexts: Dataset Collection
  and the Role of Supervised Attention   Abusive language is a massive problem in online social platforms. Existing
abusive language detection techniques are particularly ill-suited to comments
containing heterogeneous abusive language patterns, i.e., both abusive and
non-abusive parts. This is due in part to the lack of datasets that explicitly
annotate heterogeneity in abusive language. We tackle this challenge by
providing an annotated dataset of abusive language in over 11,000 comments from
YouTube. We account for heterogeneity in this dataset by separately annotating
both the comment as a whole and the individual sentences that comprise each
comment. We then propose an algorithm that uses a supervised attention
mechanism to detect and categorize abusive content using multi-task learning.
We empirically demonstrate the challenges of using traditional techniques on
heterogeneous content and the comparative gains in performance of the proposed
approach over state-of-the-art methods.

16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys