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        <title>Frugal neural reranking: evaluation on the Covid-19 literature</title>
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    <name type="personal">
        <namePart type="given">Tiago</namePart>
        <namePart type="family">Almeida</namePart>
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    <name type="personal">
        <namePart type="given">Sérgio</namePart>
        <namePart type="family">Matos</namePart>
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            <title>Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020</title>
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        <name type="personal">
            <namePart type="given">Karin</namePart>
            <namePart type="family">Verspoor</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Michael</namePart>
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            <namePart type="given">Berry</namePart>
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        <name type="personal">
            <namePart type="given">Mark</namePart>
            <namePart type="family">Dredze</namePart>
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        <name type="personal">
            <namePart type="given">Rada</namePart>
            <namePart type="family">Mihalcea</namePart>
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            <namePart type="given">Byron</namePart>
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    <abstract>The Covid-19 pandemic urged the scientific community to join efforts at an unprecedented scale, leading to faster than ever dissemination of data and results, which in turn motivated more research works. This paper presents and discusses information retrieval models aimed at addressing the challenge of searching the large number of publications that stem from these studies. The model presented, based on classical baselines followed by an interaction based neural ranking model, was evaluated and evolved within the TREC Covid challenge setting. Results on this dataset show that, when starting with a strong baseline, our light neural ranking model can achieve results that are comparable to other model architectures that use very large number of parameters.</abstract>
    <identifier type="citekey">almeida-matos-2020-frugal</identifier>
    <identifier type="doi">10.18653/v1/2020.nlpcovid19-2.3</identifier>
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        <url>https://aclanthology.org/2020.nlpcovid19-2.3</url>
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%0 Conference Proceedings
%T Frugal neural reranking: evaluation on the Covid-19 literature
%A Almeida, Tiago
%A Matos, Sérgio
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F almeida-matos-2020-frugal
%X The Covid-19 pandemic urged the scientific community to join efforts at an unprecedented scale, leading to faster than ever dissemination of data and results, which in turn motivated more research works. This paper presents and discusses information retrieval models aimed at addressing the challenge of searching the large number of publications that stem from these studies. The model presented, based on classical baselines followed by an interaction based neural ranking model, was evaluated and evolved within the TREC Covid challenge setting. Results on this dataset show that, when starting with a strong baseline, our light neural ranking model can achieve results that are comparable to other model architectures that use very large number of parameters.
%R 10.18653/v1/2020.nlpcovid19-2.3
%U https://aclanthology.org/2020.nlpcovid19-2.3
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.3

Markdown (Informal)

[Frugal neural reranking: evaluation on the Covid-19 literature](https://aclanthology.org/2020.nlpcovid19-2.3) (Almeida & Matos, NLP-COVID19 2020)

• Frugal neural reranking: evaluation on the Covid-19 literature (Almeida & Matos, NLP-COVID19 2020)

ACL

• Tiago Almeida and Sérgio Matos. 2020. Frugal neural reranking: evaluation on the Covid-19 literature. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.