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Neural Machine Translation (NMT) models have demonstrated strong state of theart performance on translation tasks where well-formed training and evaluationdata are provided, but they remain sensitive to inputs that include errors ofvarious types. Specifically, in the context of long-form speech translationsystems, where the input transcripts come from Automatic Speech Recognition(ASR), the NMT models have to handle errors including phoneme substitutions,grammatical structure, and sentence boundaries, all of which pose challenges toNMT robustness. Through in-depth error analysis, we show that sentence boundarysegmentation has the largest impact on quality, and we develop a simple dataaugmentation strategy to improve segmentation robustness.

Sentence Boundary Augmentation For Neural Machine Translation Robustness