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        <title>Neural Machine Translation of Text from Non-Native Speakers</title>
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    <abstract>Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data. In this paper, we show that augmenting training data with sentences containing artificially-introduced grammatical errors can make the system more robust to such errors. In combination with an automatic grammar error correction system, we can recover 1.0 BLEU out of 2.4 BLEU lost due to grammatical errors. We also present a set of Spanish translations of the JFLEG grammar error correction corpus, which allows for testing NMT robustness to real grammatical errors.</abstract>
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%0 Conference Proceedings
%T Neural Machine Translation of Text from Non-Native Speakers
%A Anastasopoulos, Antonios
%A Lui, Alison
%A Nguyen, Toan Q.
%A Chiang, David
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F anastasopoulos-etal-2019-neural
%X Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data. In this paper, we show that augmenting training data with sentences containing artificially-introduced grammatical errors can make the system more robust to such errors. In combination with an automatic grammar error correction system, we can recover 1.0 BLEU out of 2.4 BLEU lost due to grammatical errors. We also present a set of Spanish translations of the JFLEG grammar error correction corpus, which allows for testing NMT robustness to real grammatical errors.
%R 10.18653/v1/N19-1311
%U https://aclanthology.org/N19-1311
%U https://doi.org/10.18653/v1/N19-1311
%P 3070-3080

Markdown (Informal)

[Neural Machine Translation of Text from Non-Native Speakers](https://aclanthology.org/N19-1311) (Anastasopoulos et al., NAACL 2019)

• Neural Machine Translation of Text from Non-Native Speakers (Anastasopoulos et al., NAACL 2019)

ACL

• Antonios Anastasopoulos, Alison Lui, Toan Q. Nguyen, and David Chiang. 2019. Neural Machine Translation of Text from Non-Native Speakers. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3070–3080, Minneapolis, Minnesota. Association for Computational Linguistics.