# Syntax-aware Data Augmentation for Neural Machine Translation

arXiv: https://arxiv.org/abs/2004.14200 · HTML: https://mlsanity.com/paper/2004.14200/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 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)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation](https://mlsanity.com/paper/2205.08722/) (2022)
- [TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping](https://mlsanity.com/paper/2311.02355/) (2023)
- [Developing neural machine translation models for Hungarian-English](https://mlsanity.com/paper/2111.04099/) (2021)
- [Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network](https://mlsanity.com/paper/2111.11707/) (2021)
- [Syntax-based data augmentation for Hungarian-English machine translation](https://mlsanity.com/paper/2201.06876/) (2022)
- [Data Augmentation for Machine Translation via Dependency Subtree Swapping](https://mlsanity.com/paper/2307.07025/) (2023)
- [Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring](https://mlsanity.com/paper/2507.18293/) (2025)

## Abstract (arXiv)

Data augmentation is an effective performance enhancement in neural machine translation (NMT) by generating additional bilingual data. In this paper, we propose a novel data augmentation enhancement strategy for neural machine translation. Different from existing data augmentation methods which simply choose words with the same probability across different sentences for modification, we set sentence-specific probability for word selection by considering their roles in sentence. We use dependency parse tree of input sentence as an effective clue to determine selecting probability for every words in each sentence. Our proposed method is evaluated on WMT14 English-to-German dataset and IWSLT14 German-to-English dataset. The result of extensive experiments show our proposed syntax-aware data augmentation method may effectively boost existing sentence-independent methods for significant translation performance improvement.