Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with
  Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets   Twitter and, in general, social media has become an indispensable
communication channel in times of emergency. The ubiquitousness of smartphone
gadgets enables people to declare an emergency observed in real-time. As a
result, more agencies are interested in programmatically monitoring Twitter
(disaster relief organizations and news agencies). Therefore, recognizing the
informativeness of a Tweet can help filter noise from the large volumes of
Tweets. In this paper, we present our submission for WNUT-2020 Task 2:
Identification of informative COVID-19 English Tweets. Our most successful
model is an ensemble of transformers, including RoBERTa, XLNet, and BERTweet
trained in a Semi-Supervised Learning (SSL) setting. The proposed system
achieves an F1 score of 0.9011 on the test set (ranking 7th on the leaderboard)
and shows significant gains in performance compared to a baseline system using
FastText embeddings.

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The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: solv-int
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg