AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages

arXiv:2606.12708 · cs.CL, cs.AI · Submitted 2026-06-10 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-06-10

Updated: 2026-09-01

License: http://creativecommons.org/licenses/by/4.0/

The gist: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP.

Terminology

Universal Dependencies — 17× in this paper
Cross-lingual Transfer — 11× in this paper · explained in 2 episodes
Transformer — 10× in this paper · explained in 2 episodes
Benchmark — 8× in this paper · explained in 2 episodes
Copula — 6× in this paper · explained in 2 episodes
Accuracy — 5× in this paper · explained in 1 episode
European Language Resources Association — 5× in this paper
Adam — 4× in this paper · explained in 2 episodes
Consistency — 4× in this paper · explained in 3 episodes
Embeddings — 3× in this paper · explained in 2 episodes
Fine-Tuning — 3× in this paper · explained in 5 episodes
Adaptation — 2× in this paper · explained in 1 episode
Representation Learning — 2× in this paper · explained in 2 episodes
Using the Surface-Syntactic Universal Dependencies — 2× in this paper
Annotator Agreement — 1× in this paper · explained in 1 episode
Core — 1× in this paper · explained in 2 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
Low-Resource Languages — 1× in this paper · explained in 4 episodes
Tokenization — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes

Abstract

Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP. We aim to bridge this gap by introducing AfriSUD, the first large-scale collection of syntactically annotated treebanks for nine diverse African languages spanning major language families and regions across Sub-Saharan Africa. Using the Surface-Syntactic Universal Dependencies (SUD) framework, our community-led effort provides high-quality, native-speaker verified data that capture typological key features such as agglutination and tone. We evaluate a range of models on AfriSUD for part-of-speech tagging and dependency parsing including non-transformer baselines, multilingual pretrained encoders, and LLMs. Our results reveal a significant syntax gap, where models still show clear limitations across the nine languages, suggesting that existing architectures may not fully capture the structural diversity of African-language syntax.

AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages

Terminology

Abstract

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