| Paper ID | HLT-8.5 |
| Paper Title |
SENTENCE BOUNDARY AUGMENTATION FOR NEURAL MACHINE TRANSLATION ROBUSTNESS |
| Authors |
Daniel Li, Columbia University, United States; Te I, Naveen Arivazhagan, Colin Cherry, Dirk Padfield, Google, United States |
| Session | HLT-8: Speech Translation 2: Aspects |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-MTSW] Machine Translation for Spoken and Written Language |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
