Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Document Retrieval for Deep Question Answering   State-of-the-art systems in deep question answering proceed as follows: (1)
an initial document retrieval selects relevant documents, which (2) are then
processed by a neural network in order to extract the final answer. Yet the
exact interplay between both components is poorly understood, especially
concerning the number of candidate documents that should be retrieved. We show
that choosing a static number of documents -- as used in prior research --
suffers from a noise-information trade-off and yields suboptimal results. As a
remedy, we propose an adaptive document retrieval model. This learns the
optimal candidate number for document retrieval, conditional on the size of the
corpus and the query. We report extensive experimental results showing that our
adaptive approach outperforms state-of-the-art methods on multiple benchmark
datasets, as well as in the context of corpora with variable sizes.

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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: eess
5
The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat