AfriSUD: New Dependency Treebank Collection Addresses NLP Gap for African Languages
Researchers have introduced AfriSUD, the first large-scale collection of syntactically annotated treebanks for nine African languages, using the Surface-Syntactic Universal Dependencies framework. The dataset was created through community-led effort with native-speaker verification to capture linguistic features like agglutination and tone. Evaluation of multiple model types reveals significant limitations in existing NLP architectures for African-language syntax, highlighting the need for better resources and approaches.
AfriSUD represents a major effort to address the underrepresentation of African languages in NLP research and resources. The collection includes high-quality, syntactically annotated data for nine diverse African languages spanning major language families and regions across Sub-Saharan Africa, created using the Surface-Syntactic Universal Dependencies (SUD) framework. The dataset was developed through community collaboration with native-speaker verification to ensure accuracy and capture important typological features such as agglutination and tone. The researchers evaluated a range of models on AfriSUD for part-of-speech tagging and dependency parsing, including non-transformer baselines, multilingual pretrained encoders, and large language models. Their results reveal what they term a "syntax gap," demonstrating that existing model architectures show clear limitations across all nine languages and may not fully capture the structural diversity of African-language syntax.
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- arXiv cs.AICenter
AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages
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