Improving Translation Robustness with Visual Cues and Error Correction

Zhenhao Li, Marek Rei, Lucia Specia

Neural Machine Translation models are brittle to input noise. Current robustness techniques mostly adapt models to existing noisy texts, but these models generally fail when faced with unseen noise and their performance degrades on clean texts. In this paper, we introduce the idea of visual context to improve translation robustness against noisy texts. In addition, we propose a novel error correction training regime by treating error correction as an auxiliary task to further improve robustness. Experiments on English-French and English-German translation show that both multimodality and error correction training are beneficial for model robustness to known and new types of errors, while keeping the quality on clean texts.

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