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    <titleInfo>
        <title>Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension</title>
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    <name type="personal">
        <namePart type="given">Denis</namePart>
        <namePart type="family">Smirnov</namePart>
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            <title>Proceedings of the Student Research Workshop Associated with RANLP 2019</title>
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        <name type="personal">
            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Irina</namePart>
            <namePart type="family">Temnikova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Branislava</namePart>
            <namePart type="family">Šandrih</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>State-of-the-art machine reading comprehension models are capable of producing answers for factual questions about a given piece of text. However, some type of questions requires commonsense knowledge which cannot be inferred from the given text passage. Thus, external semantic information could enhance the performance of these models. This PhD research proposal provides a brief overview of some existing machine reading comprehension datasets and models and outlines possible ways of their improvement.</abstract>
    <identifier type="citekey">smirnov-2019-neural</identifier>
    <identifier type="doi">10.26615/issn.2603-2821.2019_014</identifier>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension
%A Smirnov, Denis
%Y Kovatchev, Venelin
%Y Temnikova, Irina
%Y Šandrih, Branislava
%Y Nikolova, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2019
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F smirnov-2019-neural
%X State-of-the-art machine reading comprehension models are capable of producing answers for factual questions about a given piece of text. However, some type of questions requires commonsense knowledge which cannot be inferred from the given text passage. Thus, external semantic information could enhance the performance of these models. This PhD research proposal provides a brief overview of some existing machine reading comprehension datasets and models and outlines possible ways of their improvement.
%R 10.26615/issn.2603-2821.2019_014
%U https://aclanthology.org/R19-2014
%U https://doi.org/10.26615/issn.2603-2821.2019_014
%P 90-94

Markdown (Informal)

[Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension](https://aclanthology.org/R19-2014) (Smirnov, RANLP 2019)

• Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension (Smirnov, RANLP 2019)

ACL

• Denis Smirnov. 2019. Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension. In Proceedings of the Student Research Workshop Associated with RANLP 2019, pages 90–94, Varna, Bulgaria. INCOMA Ltd..