  Neural Machine Translation (NMT) models have demonstrated strong state of the
art performance on translation tasks where well-formed training and evaluation
data are provided, but they remain sensitive to inputs that include errors of
various types. Specifically, in the context of long-form speech translation
systems, 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 to
NMT robustness. Through in-depth error analysis, we show that sentence boundary
segmentation has the largest impact on quality, and we develop a simple data
augmentation strategy to improve segmentation robustness.
