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        <title>Evaluating Robustness to Input Perturbations for Neural Machine Translation</title>
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        <namePart type="given">Xing</namePart>
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    <name type="personal">
        <namePart type="given">Yaser</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing subword regularization to address robustness and perform extensive evaluations of these models using the robustness measures proposed. Results show that our proposed metrics reveal a clear trend of improved robustness to perturbations when subword regularization methods are used.</abstract>
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%0 Conference Proceedings
%T Evaluating Robustness to Input Perturbations for Neural Machine Translation
%A Niu, Xing
%A Mathur, Prashant
%A Dinu, Georgiana
%A Al-Onaizan, Yaser
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F niu-etal-2020-evaluating
%X Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing subword regularization to address robustness and perform extensive evaluations of these models using the robustness measures proposed. Results show that our proposed metrics reveal a clear trend of improved robustness to perturbations when subword regularization methods are used.
%R 10.18653/v1/2020.acl-main.755
%U https://aclanthology.org/2020.acl-main.755/
%U https://doi.org/10.18653/v1/2020.acl-main.755
%P 8538-8544

Markdown (Informal)

[Evaluating Robustness to Input Perturbations for Neural Machine Translation](https://aclanthology.org/2020.acl-main.755/) (Niu et al., ACL 2020)

• Evaluating Robustness to Input Perturbations for Neural Machine Translation (Niu et al., ACL 2020)

ACL

• Xing Niu, Prashant Mathur, Georgiana Dinu, and Yaser Al-Onaizan. 2020. Evaluating Robustness to Input Perturbations for Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8538–8544, Online. Association for Computational Linguistics.