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        <title>On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation</title>
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        <namePart type="given">Chaojun</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>The standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is under debate. In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift. In experiments on three datasets with multiple test domains, we show that exposure bias is partially to blame for hallucinations, and that training with Minimum Risk Training, which avoids exposure bias, can mitigate this. Our analysis explains why exposure bias is more problematic under domain shift, and also links exposure bias to the beam search problem, i.e. performance deterioration with increasing beam size. Our results provide a new justification for methods that reduce exposure bias: even if they do not increase performance on in-domain test sets, they can increase model robustness to domain shift.</abstract>
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%0 Conference Proceedings
%T On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation
%A Wang, Chaojun
%A Sennrich, Rico
%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 wang-sennrich-2020-exposure
%X The standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is under debate. In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift. In experiments on three datasets with multiple test domains, we show that exposure bias is partially to blame for hallucinations, and that training with Minimum Risk Training, which avoids exposure bias, can mitigate this. Our analysis explains why exposure bias is more problematic under domain shift, and also links exposure bias to the beam search problem, i.e. performance deterioration with increasing beam size. Our results provide a new justification for methods that reduce exposure bias: even if they do not increase performance on in-domain test sets, they can increase model robustness to domain shift.
%R 10.18653/v1/2020.acl-main.326
%U https://aclanthology.org/2020.acl-main.326
%U https://doi.org/10.18653/v1/2020.acl-main.326
%P 3544-3552

Markdown (Informal)

[On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation](https://aclanthology.org/2020.acl-main.326) (Wang & Sennrich, ACL 2020)

• On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation (Wang & Sennrich, ACL 2020)

ACL

• Chaojun Wang and Rico Sennrich. 2020. On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3544–3552, Online. Association for Computational Linguistics.