mlsanityData to 14 Oct 2026

Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages

arXiv 2210.10692 ↗First posted 19 Oct 2022cs.CLcited by 6

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Abstract (arXiv)

We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fine-tuning a pre-trained language model. To train the classifier, we obtain positive samples (i.e. high-quality parallel sentences) from a gold-standard curated dataset and extract negative samples (i.e. low-quality parallel sentences) from automatically aligned parallel data by choosing sentences with low alignment scores. Our final machine translation model was then trained on filtered data, instead of the entire noisy dataset. We empirically validate our approach by evaluating on two common datasets and show that data filtering generally improves overall translation quality, in some cases even significantly.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 32 of its 74 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

32 of 74 references.

References · 32

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20 more
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 3.

6 papers.

  1. 2023SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)cited by 41
  2. 2024Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruningcited by 10
  3. 2025The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languagescited by 7
  4. 2025Synthetic Voice Data for Automatic Speech Recognition in African Languagescited by 3
  5. 2025HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processingcited by 2
  6. 2023Leveraging Closed-Access Multilingual Embedding for Automatic Sentence Alignment in Low Resource Languagescited by 0