# Sentence Boundary Augmentation For Neural Machine Translation Robustness

arXiv: https://arxiv.org/abs/2010.11132 · HTML: https://mlsanity.com/paper/2010.11132/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [From Simultaneous to Streaming Machine Translation by Leveraging Streaming History](https://mlsanity.com/paper/2203.02459/) (2022)
- [Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation](https://mlsanity.com/paper/2210.13363/) (2022)
- [Simultaneous Translation for Unsegmented Input: A Sliding Window Approach](https://mlsanity.com/paper/2210.09754/) (2022)
- [StreamUni: Achieving Streaming Speech Translation with a Unified Large Speech-Language Model](https://mlsanity.com/paper/2507.07803/) (2025)
- [Segmentation-Free Streaming Machine Translation](https://mlsanity.com/paper/2309.14823/) (2023)
- [Improved Long-Form Spoken Language Translation with Large Language Models](https://mlsanity.com/paper/2212.09895/) (2022)
- [Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model](https://mlsanity.com/paper/2505.11421/) (2025)
- [Overcoming Latency Bottlenecks in On-Device Speech Translation: A Cascaded Approach with Alignment-Based Streaming MT](https://mlsanity.com/paper/2508.13358/) (2025)
- [Leveraging Sentence-oriented Augmentation and Transformer-Based Architecture for Vietnamese-Bahnaric Translation](https://mlsanity.com/paper/2601.19124/) (2026)

## Abstract (arXiv)

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.