Sentence Boundary Augmentation For Neural Machine Translation Robustness
This addresses robustness issues in NMT for speech translation systems, but it is incremental as it focuses on a specific error type with a simple augmentation method.
The paper tackled the problem of Neural Machine Translation (NMT) models being sensitive to sentence boundary errors in long-form speech translation, and developed a data augmentation strategy that improved robustness, though no concrete numbers were provided.
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.