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Domain-specific MT for Low-resource Languages: The case of Bambara-French

arXiv 2104.00041 ↗First posted 31 Mar 2021cs.CLcited by 4

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Abstract (arXiv)

Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-specific MT for Bambara, an under-resourced Mande language spoken in Mali. We present the first domain-specific parallel dataset for MT of Bambara into and from French. We discuss challenges in working with small quantities of domain-specific data for a low-resource language and we present the results of machine learning experiments on this data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

11 of 22 references.

References · 11

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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.

4 papers.

  1. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  2. 2022Building Machine Translation Systems for the Next Thousand Languagescited by 69
  3. 2021The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translationcited by 46
  4. 2025SMOL: Professionally translated parallel data for 115 under-represented languagescited by 11