Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Seeing Things from a Different Angle: Discovering Diverse Perspectives
  about Claims   One key consequence of the information revolution is a significant increase
and a contamination of our information supply. The practice of fact checking
won't suffice to eliminate the biases in text data we observe, as the degree of
factuality alone does not determine whether biases exist in the spectrum of
opinions visible to us. To better understand controversial issues, one needs to
view them from a diverse yet comprehensive set of perspectives. For example,
there are many ways to respond to a claim such as "animals should have lawful
rights", and these responses form a spectrum of perspectives, each with a
stance relative to this claim and, ideally, with evidence supporting it.
Inherently, this is a natural language understanding task, and we propose to
address it as such. Specifically, we propose the task of substantiated
perspective discovery where, given a claim, a system is expected to discover a
diverse set of well-corroborated perspectives that take a stance with respect
to the claim. Each perspective should be substantiated by evidence paragraphs
which summarize pertinent results and facts. We construct PERSPECTRUM, a
dataset of claims, perspectives and evidence, making use of online debate
websites to create the initial data collection, and augmenting it using search
engines in order to expand and diversify our dataset. We use crowd-sourcing to
filter out noise and ensure high-quality data. Our dataset contains 1k claims,
accompanied with pools of 10k and 8k perspective sentences and evidence
paragraphs, respectively. We provide a thorough analysis of the dataset to
highlight key underlying language understanding challenges, and show that human
baselines across multiple subtasks far outperform ma-chine baselines built upon
state-of-the-art NLP techniques. This poses a challenge and opportunity for the
NLP community to address.

24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg