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        <title>Visual Cues and Error Correction for Translation Robustness</title>
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        <namePart type="given">Zhenhao</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
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    <abstract>Neural Machine Translation models are sensitive to noise in the input texts, such as misspelled words and ungrammatical constructions. Existing robustness techniques generally fail when faced with unseen types of noise and their performance degrades on clean texts. In this paper, we focus on three types of realistic noise that are commonly generated by humans and introduce the idea of visual context to improve translation robustness for noisy texts. In addition, we describe a novel error correction training regime that can be used as an auxiliary task to further improve translation robustness. Experiments on English-French and English-German translation show that both multimodal and error correction components improve model robustness to noisy texts, while still retaining translation quality on clean texts.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.271</identifier>
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%0 Conference Proceedings
%T Visual Cues and Error Correction for Translation Robustness
%A Li, Zhenhao
%A Rei, Marek
%A Specia, Lucia
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F li-etal-2021-visual-cues
%X Neural Machine Translation models are sensitive to noise in the input texts, such as misspelled words and ungrammatical constructions. Existing robustness techniques generally fail when faced with unseen types of noise and their performance degrades on clean texts. In this paper, we focus on three types of realistic noise that are commonly generated by humans and introduce the idea of visual context to improve translation robustness for noisy texts. In addition, we describe a novel error correction training regime that can be used as an auxiliary task to further improve translation robustness. Experiments on English-French and English-German translation show that both multimodal and error correction components improve model robustness to noisy texts, while still retaining translation quality on clean texts.
%R 10.18653/v1/2021.findings-emnlp.271
%U https://aclanthology.org/2021.findings-emnlp.271
%U https://doi.org/10.18653/v1/2021.findings-emnlp.271
%P 3153-3168

Markdown (Informal)

[Visual Cues and Error Correction for Translation Robustness](https://aclanthology.org/2021.findings-emnlp.271) (Li et al., Findings 2021)

• Visual Cues and Error Correction for Translation Robustness (Li et al., Findings 2021)

ACL

• Zhenhao Li, Marek Rei, and Lucia Specia. 2021. Visual Cues and Error Correction for Translation Robustness. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3153–3168, Punta Cana, Dominican Republic. Association for Computational Linguistics.