Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web
  to Special Domain Search   Neural rankers based on deep pretrained language models (LMs) have been shown
to improve many information retrieval benchmarks. However, these methods are
affected by their the correlation between pretraining domain and target domain
and rely on massive fine-tuning relevance labels. Directly applying pretraining
methods to specific domains may result in suboptimal search quality because
specific domains may have domain adaption problems, such as the COVID domain.
This paper presents a search system to alleviate the special domain adaption
problem. The system utilizes the domain-adaptive pretraining and few-shot
learning technologies to help neural rankers mitigate the domain discrepancy
and label scarcity problems. Besides, we also integrate dense retrieval to
alleviate traditional sparse retrieval's vocabulary mismatch obstacle. Our
system performs the best among the non-manual runs in Round 2 of the TREC-COVID
task, which aims to retrieve useful information from scientific literature
related to COVID-19. Our code is publicly available at
https://github.com/thunlp/OpenMatch.

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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int