Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Composite Re-Ranking for Efficient Document Search with BERT   Although considerable efforts have been devoted to transformer-based ranking
models for document search, the relevance-efficiency tradeoff remains a
critical problem for ad-hoc ranking. To overcome this challenge, this paper
presents BECR (BERT-based Composite Re-Ranking), a composite re-ranking scheme
that combines deep contextual token interactions and traditional lexical
term-matching features. In particular, BECR exploits a token encoding mechanism
to decompose the query representations into pre-computable uni-grams and
skip-n-grams. By applying token encoding on top of a dual-encoder architecture,
BECR separates the attentions between a query and a document while capturing
the contextual semantics of a query. In contrast to previous approaches, this
framework does not perform expensive BERT computations during online inference.
Thus, it is significantly faster, yet still able to achieve high
competitiveness in ad-hoc ranking relevance. Finally, an in-depth comparison
between BECR and other start-of-the-art neural ranking baselines is described
using the TREC datasets, thereby further demonstrating the enhanced relevance
and efficiency of BECR.

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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin