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        <title>Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement</title>
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        <namePart type="given">Nina</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1032</identifier>
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%0 Conference Proceedings
%T Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
%A Poerner, Nina
%A Schütze, Hinrich
%A Roth, Benjamin
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F poerner-etal-2018-evaluating
%X The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.
%R 10.18653/v1/P18-1032
%U https://aclanthology.org/P18-1032
%U https://doi.org/10.18653/v1/P18-1032
%P 340-350

Markdown (Informal)

[Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement](https://aclanthology.org/P18-1032) (Poerner et al., ACL 2018)

• Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement (Poerner et al., ACL 2018)

ACL

• Nina Poerner, Hinrich Schütze, and Benjamin Roth. 2018. Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 340–350, Melbourne, Australia. Association for Computational Linguistics.