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

arXiv: https://arxiv.org/abs/2606.12708 · HTML: https://mlsanity.com/paper/2606.12708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 58 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go](https://mlsanity.com/paper/2203.08351/) (2022)
- [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021)
- [Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://mlsanity.com/paper/2104.00290/) (2021)
- [Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations](https://mlsanity.com/paper/1910.05479/) (2019)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages](https://mlsanity.com/paper/2502.09814/) (2025)
- [Lugha-Llama: Adapting Large Language Models for African Languages](https://mlsanity.com/paper/2504.06536/) (2025)
- [Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction](https://mlsanity.com/paper/2305.12258/) (2023)
- [Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages](https://mlsanity.com/paper/2511.06531/) (2025)
- [Neural Machine Translation with Dynamic Graph Convolutional Decoder](https://mlsanity.com/paper/2305.17698/) (2023)
- [Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers](https://mlsanity.com/paper/2004.07642/) (2020)
- [Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers](https://mlsanity.com/paper/1909.09279/) (2019)

## Built on by (most cited first, 1 of 1)

- [Morpho-VITS: Variational Inference with Morphological Modeling for End-to-End Speech Synthesis of a Tonal Bantu Language](https://mlsanity.com/paper/2609.24310/) (2026)

## Abstract (arXiv)

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.