Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Taking a Stance on Fake News: Towards Automatic Disinformation
  Assessment via Deep Bidirectional Transformer Language Models for Stance
  Detection   The exponential rise of social media and digital news in the past decade has
had the unfortunate consequence of escalating what the United Nations has
called a global topic of concern: the growing prevalence of disinformation.
Given the complexity and time-consuming nature of combating disinformation
through human assessment, one is motivated to explore harnessing AI solutions
to automatically assess news articles for the presence of disinformation. A
valuable first step towards automatic identification of disinformation is
stance detection, where given a claim and a news article, the aim is to predict
if the article agrees, disagrees, takes no position, or is unrelated to the
claim. Existing approaches in literature have largely relied on hand-engineered
features or shallow learned representations (e.g., word embeddings) to encode
the claim-article pairs, which can limit the level of representational
expressiveness needed to tackle the high complexity of disinformation
identification. In this work, we explore the notion of harnessing large-scale
deep bidirectional transformer language models for encoding claim-article pairs
in an effort to construct state-of-the-art stance detection geared for
identifying disinformation. Taking advantage of bidirectional cross-attention
between claim-article pairs via pair encoding with self-attention, we construct
a large-scale language model for stance detection by performing transfer
learning on a RoBERTa deep bidirectional transformer language model, and were
able to achieve state-of-the-art performance (weighted accuracy of 90.01%) on
the Fake News Challenge Stage 1 (FNC-I) benchmark. These promising results
serve as motivation for harnessing such large-scale language models as powerful
building blocks for creating effective AI solutions to combat disinformation.

21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin