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        <title>The Unreasonable Volatility of Neural Machine Translation Models</title>
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    <name type="personal">
        <namePart type="given">Marzieh</namePart>
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        <namePart type="given">Christof</namePart>
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        <dateIssued>2020-07</dateIssued>
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            <title>Proceedings of the Fourth Workshop on Neural Generation and Translation</title>
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        <name type="personal">
            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Birch</namePart>
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        <name type="personal">
            <namePart type="given">Andrew</namePart>
            <namePart type="family">Finch</namePart>
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        <name type="personal">
            <namePart type="given">Hiroaki</namePart>
            <namePart type="family">Hayashi</namePart>
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        <name type="personal">
            <namePart type="given">Kenneth</namePart>
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        <name type="personal">
            <namePart type="given">Marcin</namePart>
            <namePart type="family">Junczys-Dowmunt</namePart>
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            <namePart type="given">Ioannis</namePart>
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        <name type="personal">
            <namePart type="given">Xian</namePart>
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            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Oda</namePart>
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    <abstract>Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered. We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We discover that with trivial modifications of source sentences, we can identify cases where unexpected changes happen in the translation and in the worst case lead to mistranslations. This volatile behavior of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models. We find that both RNN and Transformer models display volatile behavior in 26% and 19% of sentence variations, respectively.</abstract>
    <identifier type="citekey">fadaee-monz-2020-unreasonable</identifier>
    <identifier type="doi">10.18653/v1/2020.ngt-1.10</identifier>
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        <date>2020-07</date>
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            <start>88</start>
            <end>96</end>
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%0 Conference Proceedings
%T The Unreasonable Volatility of Neural Machine Translation Models
%A Fadaee, Marzieh
%A Monz, Christof
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Hayashi, Hiroaki
%Y Heafield, Kenneth
%Y Junczys-Dowmunt, Marcin
%Y Konstas, Ioannis
%Y Li, Xian
%Y Neubig, Graham
%Y Oda, Yusuke
%S Proceedings of the Fourth Workshop on Neural Generation and Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F fadaee-monz-2020-unreasonable
%X Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered. We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We discover that with trivial modifications of source sentences, we can identify cases where unexpected changes happen in the translation and in the worst case lead to mistranslations. This volatile behavior of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models. We find that both RNN and Transformer models display volatile behavior in 26% and 19% of sentence variations, respectively.
%R 10.18653/v1/2020.ngt-1.10
%U https://aclanthology.org/2020.ngt-1.10
%U https://doi.org/10.18653/v1/2020.ngt-1.10
%P 88-96

Markdown (Informal)

[The Unreasonable Volatility of Neural Machine Translation Models](https://aclanthology.org/2020.ngt-1.10) (Fadaee & Monz, NGT 2020)

• The Unreasonable Volatility of Neural Machine Translation Models (Fadaee & Monz, NGT 2020)

ACL

• Marzieh Fadaee and Christof Monz. 2020. The Unreasonable Volatility of Neural Machine Translation Models. In Proceedings of the Fourth Workshop on Neural Generation and Translation, pages 88–96, Online. Association for Computational Linguistics.