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        <title>Adaptive Document Retrieval for Deep Question Answering</title>
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        <namePart type="given">Bernhard</namePart>
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        <namePart type="given">Stefan</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1055</identifier>
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        <date>2018-oct-nov</date>
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            <start>576</start>
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%0 Conference Proceedings
%T Adaptive Document Retrieval for Deep Question Answering
%A Kratzwald, Bernhard
%A Feuerriegel, Stefan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kratzwald-feuerriegel-2018-adaptive
%X 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.
%R 10.18653/v1/D18-1055
%U https://aclanthology.org/D18-1055
%U https://doi.org/10.18653/v1/D18-1055
%P 576-581

Markdown (Informal)

[Adaptive Document Retrieval for Deep Question Answering](https://aclanthology.org/D18-1055) (Kratzwald & Feuerriegel, EMNLP 2018)

• Adaptive Document Retrieval for Deep Question Answering (Kratzwald & Feuerriegel, EMNLP 2018)

ACL

• Bernhard Kratzwald and Stefan Feuerriegel. 2018. Adaptive Document Retrieval for Deep Question Answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 576–581, Brussels, Belgium. Association for Computational Linguistics.