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Improving Robustness of Machine Translation with Synthetic Noise
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Modern Machine Translation (MT) systems perform consistently well on clean, in-domain text. However most human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of output translation. In this paper we leverage the Machine Translation of Noisy Text (MTNT) dataset to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system resilient to naturally occurring noise and partially mitigate loss in accuracy resulting therefrom.
Forward citations
Cited by 2 Pith papers
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Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report
Baidu-OSU WMT19 system achieves >10 BLEU gain on En-Fr and Fr-En social media translation via domain sensitive training and pseudo noisy sources.
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CUNI System for the WMT19 Robustness Task
A pre-trained Transformer MT system outperforms an LSTM baseline on noisy text and gains further robustness from fine-tuning on noisy data without harming clean-text performance.
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