---
title: "Generalized Data Augmentation for Low-Resource Translation"
description: "Xia et al. propose a unified augmentation framework that draws on every available… 1.1 BLEU. 38 references, cited by 69 papers here."
authors: ["Mengzhou Xia", "Xiang Kong", "Antonios Anastasopoulos", "Graham Neubig"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1579/"
doi: "10.18653/v1/p19-1579"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xia-2019-generalized-data-augmentation-low-resource/"
---
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# Generalized Data Augmentation for Low-Resource Translation

**Authors:** [Mengzhou Xia](https://md.research.elamur.ai/free/en/authors/mengzhou-xia/), [Xiang Kong](https://md.research.elamur.ai/free/en/authors/xiang-kong/), [Antonios Anastasopoulos](https://md.research.elamur.ai/free/en/authors/antonios-anastasopoulos/) and [Graham Neubig](https://md.research.elamur.ai/free/en/authors/graham-neubig/) · **Year:** 2019 · **Venue:** [Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2019](https://md.research.elamur.ai/free/en/venues/acl/2019/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P19-1579/) · [PDF](https://aclanthology.org/P19-1579.pdf) · [DOI 10.18653/v1/p19-1579](https://doi.org/10.18653/v1/p19-1579) · [OpenAlex](https://openalex.org/W2951476960)

**Citations:** 108 citations (OpenAlex, 2026-10-14) · [69](https://md.research.elamur.ai/free/en/papers/xia-2019-generalized-data-augmentation-low-resource/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Neural MT for low-resource languages (LRLs) is hampered by tiny parallel corpora. Ordinary back-translation also helps little here, because the reverse model is itself trained on scarce data. Xia et al. propose a unified augmentation framework that draws on every available resource: English monolingual text, parallel data from a closely related high-resource language (HRL, e.g. Turkish for Azerbaijani), and monolingual HRL/LRL corpora. Beyond back-translating English into the LRL, they also back-translate English into the HRL. Their main contribution is turning HRL–ENG data into pseudo-LRL–ENG data through a two-stage pivot. First, HRL words are replaced with LRL equivalents taken from a dictionary induced by aligning word embeddings. Second, a modified unsupervised MT system (M-UMT) rewrites these substituted sentences so they look more like genuine LRL text. On four low-resource language pairs under extreme data scarcity, the pivot-generated data outperforms supervised back-translation by about 1.5–8 BLEU. The method is cheap and independent of model architecture, which makes it useful whenever a minority language has a well-resourced relative.

## Key points
- Four augmentation routes are compared: ENG→LRL and ENG→HRL back-translation, HRL→LRL conversion of real HRL–ENG data, and ENG→HRL→LRL conversion of back-translated data.
- The dictionary is learned by solving the Procrustes problem via SVD, using identical words as the seed lexicon and CSLS for similarity. Keeping only mutual nearest neighbours (bidirectional) generally beats unidirectional induction, with BEL as the exception.
- Word substitution followed by M-UMT exceeds plain unsupervised HRL→LRL translation by more than 2–10 BLEU. Combining English monolingual data with HRL–ENG data adds up to 1.1 BLEU more.
- The share of rare test words that augmentation "addresses" correlates strongly (Pearson) with BLEU. This suggests the gains come largely from better rare-word coverage.
- Code: github.com/xiamengzhou/DataAugForLRL (ACL 2019, Carnegie Mellon University).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 365 works · 158,048 citations

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🧭 Topics (1): [🌱 Low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · 165 works

## Cite

Cite as: Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos and Graham Neubig. 2019. Generalized Data Augmentation for Low-Resource Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xia2019generalized,
  title = {{Generalized Data Augmentation for Low-Resource Translation}},
  author = {Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos and Graham Neubig},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1579},
  url = {https://doi.org/10.18653/v1/p19-1579}
}
```

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*Updated 24 October 2026*