Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for
  Offensive Language Detection   Offensive language detection is an important and challenging task in natural
language processing. We present our submissions to the OffensEval 2020 shared
task, which includes three English sub-tasks: identifying the presence of
offensive language (Sub-task A), identifying the presence of target in
offensive language (Sub-task B), and identifying the categories of the target
(Sub-task C). Our experiments explore using a domain-tuned contextualized
language model (namely, BERT) for this task. We also experiment with different
components and configurations (e.g., a multi-view SVM) stacked upon BERT models
for specific sub-tasks. Our submissions achieve F1 scores of 91.7% in Sub-task
A, 66.5% in Sub-task B, and 63.2% in Sub-task C. We perform an ablation study
which reveals that domain tuning considerably improves the classification
performance. Furthermore, error analysis shows common misclassification errors
made by our model and outlines research directions for future.

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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: math-ph