Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text
  Retrieval   In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever
that does not require any supervised data for training. Specifically, we first
present Iterative Contrastive Learning (ICoL) that iteratively trains the query
and document encoders with a cache mechanism. ICoL not only enlarges the number
of negative instances but also keeps representations of cached examples in the
same hidden space. We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a
simple yet effective way to enhance dense retrieval with lexical matching. We
evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18
datasets of 9 zero-shot text retrieval tasks. Experimental results show that
LaPraDoR achieves state-of-the-art performance compared with supervised dense
retrieval models, and further analysis reveals the effectiveness of our
training strategy and objectives. Compared to re-ranking, our lexicon-enhanced
approach can be run in milliseconds (22.5x faster) while achieving superior
performance.

4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
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The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol