mlsanityData to 7 Oct 2026

An Exploration of Data Augmentation Techniques for Improving English to Tigrinya Translation

arXiv 2103.16789 ↗First posted 31 Mar 2021cs.CLcited by 6

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Abstract (arXiv)

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, often requiring large amounts of auxiliary data to achieve competitive results. An effective method of generating auxiliary data is back-translation of target language sentences. In this work, we present a case study of Tigrinya where we investigate several back-translation methods to generate synthetic source sentences. We find that in low-resource conditions, back-translation by pivoting through a higher-resource language related to the target language proves most effective resulting in substantial improvements over baselines.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

17 of 29 references.

References · 17

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5 more
  1. 2019Facebook FAIR's WMT19 News Translation Task Submissioncited by 179
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  3. 2018Phrase-Based & Neural Unsupervised Machine Translationcited by 138
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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 3.

6 papers.

  1. 2021Machine Translation into Low-resource Language Varietiescited by 19
  2. 2023Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunitiescited by 11
  3. 2025Natural Language Processing for Tigrinya: Current State and Future Directionscited by 2
  4. 2024EthioMT: Parallel Corpus for Low-resource Ethiopian Languagescited by 1
  5. 2025A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settingscited by 1
  6. 2025Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarkscited by 0