The study filters non-English Wikipedia, reveals quality problems, proposes a 4-level ranking, and shows filtered data matches or beats raw data in language modeling with largest gains for lower-quality editions.
Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F
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Biaffine LSTM outperforms transformer parsers like AfroXLMR and RemBERT in low-resource dependency parsing, with transformers gaining advantage as data increases and morphological complexity as a secondary predictor.
Four MAFT-based PLMs for Angolan languages report 12.3-point gains over AfroXLMR-base and 3.8-point gains over OFA baselines on downstream tasks.
citing papers explorer
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How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP
The study filters non-English Wikipedia, reveals quality problems, proposes a 4-level ranking, and shows filtered data matches or beats raw data in language modeling with largest gains for lower-quality editions.
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Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
Biaffine LSTM outperforms transformer parsers like AfroXLMR and RemBERT in low-resource dependency parsing, with transformers gaining advantage as data increases and morphological complexity as a secondary predictor.
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ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language Model
Four MAFT-based PLMs for Angolan languages report 12.3-point gains over AfroXLMR-base and 3.8-point gains over OFA baselines on downstream tasks.