# Generalized Data Augmentation for Low-Resource Translation

arXiv: https://arxiv.org/abs/1906.03785 · HTML: https://mlsanity.com/paper/1906.03785/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 30 of 53)

- [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)
- [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://mlsanity.com/paper/2402.11548/) (2024)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [Local Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2109.03856/) (2021)
- [PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks](https://mlsanity.com/paper/2202.12499/) (2022)
- [MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages](https://mlsanity.com/paper/2203.08388/) (2022)
- [Counter-Interference Adapter for Multilingual Machine Translation](https://mlsanity.com/paper/2104.08154/) (2021)
- [Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2105.03953/) (2021)
- [Data Augmentation for Sign Language Gloss Translation](https://mlsanity.com/paper/2105.07476/) (2021)
- [Towards More Fine-grained and Reliable NLP Performance Prediction](https://mlsanity.com/paper/2102.05486/) (2021)
- [Substructure Substitution: Structured Data Augmentation for NLP](https://mlsanity.com/paper/2101.00411/) (2021)
- [MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning](https://mlsanity.com/paper/2104.07908/) (2021)
- [Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications](https://mlsanity.com/paper/2306.11892/) (2023)
- [Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study](https://mlsanity.com/paper/2106.03958/) (2021)
- [DUB: Discrete Unit Back-translation for Speech Translation](https://mlsanity.com/paper/2305.11411/) (2023)
- [Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data](https://mlsanity.com/paper/2105.15071/) (2021)
- [Data Augmentation for Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2109.01758/) (2021)
- [Scaling Low-Resource MT via Synthetic Data Generation with LLMs](https://mlsanity.com/paper/2505.14423/) (2025)
- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks](https://mlsanity.com/paper/2305.16663/) (2023)
- [CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2204.00665/) (2022)
- [Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2303.15265/) (2023)
- [Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation](https://mlsanity.com/paper/2504.01919/) (2025)
- [Improved Text Classification via Test-Time Augmentation](https://mlsanity.com/paper/2206.13607/) (2022)
- [Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks](https://mlsanity.com/paper/2210.10343/) (2022)
- [Neural Machine Translation Data Generation and Augmentation using ChatGPT](https://mlsanity.com/paper/2307.05779/) (2023)
- [Rethinking Round-Trip Translation for Machine Translation Evaluation](https://mlsanity.com/paper/2209.07351/) (2022)
- [Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican](https://mlsanity.com/paper/2209.06295/) (2022)

## Abstract (arXiv)

Translation to or from low-resource languages LRLs poses challenges for machine translation in terms of both adequacy and fluency. Data augmentation utilizing large amounts of monolingual data is regarded as an effective way to alleviate these problems. In this paper, we propose a general framework for data augmentation in low-resource machine translation that not only uses target-side monolingual data, but also pivots through a related high-resource language HRL. Specifically, we experiment with a two-step pivoting method to convert high-resource data to the LRL, making use of available resources to better approximate the true data distribution of the LRL. First, we inject LRL words into HRL sentences through an induced bilingual dictionary. Second, we further edit these modified sentences using a modified unsupervised machine translation framework. Extensive experiments on four low-resource datasets show that under extreme low-resource settings, our data augmentation techniques improve translation quality by up to~1.5 to~8 BLEU points compared to supervised back-translation baselines