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        <title>Deep learning evaluation using deep linguistic processing</title>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
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            <title>Proceedings of the Workshop on Generalization in the Age of Deep Learning</title>
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        <name type="personal">
            <namePart type="given">Yonatan</namePart>
            <namePart type="family">Bisk</namePart>
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        <name type="personal">
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        <name type="personal">
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    <abstract>We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing ‘deep’ linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value on a static and monolithic dataset.</abstract>
    <identifier type="citekey">kuhnle-copestake-2018-deep</identifier>
    <identifier type="doi">10.18653/v1/W18-1003</identifier>
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%0 Conference Proceedings
%T Deep learning evaluation using deep linguistic processing
%A Kuhnle, Alexander
%A Copestake, Ann
%Y Bisk, Yonatan
%Y Levy, Omer
%Y Yatskar, Mark
%S Proceedings of the Workshop on Generalization in the Age of Deep Learning
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kuhnle-copestake-2018-deep
%X We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing ‘deep’ linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value on a static and monolithic dataset.
%R 10.18653/v1/W18-1003
%U https://aclanthology.org/W18-1003
%U https://doi.org/10.18653/v1/W18-1003
%P 17-23

Markdown (Informal)

[Deep learning evaluation using deep linguistic processing](https://aclanthology.org/W18-1003) (Kuhnle & Copestake, Gen-Deep 2018)

• Deep learning evaluation using deep linguistic processing (Kuhnle & Copestake, Gen-Deep 2018)

ACL

• Alexander Kuhnle and Ann Copestake. 2018. Deep learning evaluation using deep linguistic processing. In Proceedings of the Workshop on Generalization in the Age of Deep Learning, pages 17–23, New Orleans, Louisiana. Association for Computational Linguistics.