Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language
  Understanding Pretraining   One of the biggest challenges that prohibit the use of many current NLP
methods in clinical settings is the availability of public datasets. In this
work, we present MeDAL, a large medical text dataset curated for abbreviation
disambiguation, designed for natural language understanding pre-training in the
medical domain. We pre-trained several models of common architectures on this
dataset and empirically showed that such pre-training leads to improved
performance and convergence speed when fine-tuning on downstream medical tasks.

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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con