# MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages

arXiv: https://arxiv.org/abs/2305.13989 · HTML: https://mlsanity.com/paper/2305.13989/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [Composable Sparse Fine-Tuning for Cross-Lingual Transfer](https://mlsanity.com/paper/2110.07560/) (2021)
- [UNKs Everywhere: Adapting Multilingual Language Models to New Scripts](https://mlsanity.com/paper/2012.15562/) (2020)
- [Rethinking embedding coupling in pre-trained language models](https://mlsanity.com/paper/2010.12821/) (2020)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages](https://mlsanity.com/paper/2211.03263/) (2022)
- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022)
- [BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus](https://mlsanity.com/paper/2207.03546/) (2022)
- [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](https://mlsanity.com/paper/2010.12174/) (2020)
- [AI4D -- African Language Program](https://mlsanity.com/paper/2104.02516/) (2021)
- [Building African Voices](https://mlsanity.com/paper/2207.00688/) (2022)
- [Multilingual Open Text Release 1: Public Domain News in 44 Languages](https://mlsanity.com/paper/2201.05609/) (2022)
- [A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages](https://mlsanity.com/paper/1904.05426/) (2019)
- [Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning](https://mlsanity.com/paper/2208.03067/) (2022)

## Built on by (most cited first, 23 of 23)

- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Lugha-Llama: Adapting Large Language Models for African Languages](https://mlsanity.com/paper/2504.06536/) (2025)
- [PuoBERTa: Training and evaluation of a curated language model for Setswana](https://mlsanity.com/paper/2310.09141/) (2023)
- [NaijaRC: A Multi-choice Reading Comprehension Dataset for Nigerian Languages](https://mlsanity.com/paper/2308.09768/) (2023)
- [What is "Typological Diversity" in NLP?](https://mlsanity.com/paper/2402.04222/) (2024)
- [Can Embedding Similarity Predict Cross-Lingual Transfer? A Systematic Study on African Languages](https://mlsanity.com/paper/2601.03168/) (2026)
- [GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages](https://mlsanity.com/paper/2603.13793/) (2026)
- [The Less the Merrier? Investigating Language Representation in Multilingual Models](https://mlsanity.com/paper/2310.13228/) (2023)
- [The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment](https://mlsanity.com/paper/2405.00997/) (2024)
- [From N-grams to Pre-trained Multilingual Models For Language Identification](https://mlsanity.com/paper/2410.08728/) (2024)
- [Bridging Gaps in Natural Language Processing for Yor`ub'a: A Systematic Review of a Decade of Progress and Prospects](https://mlsanity.com/paper/2502.17364/) (2025)
- [FonMTL: Towards Multitask Learning for the Fon Language](https://mlsanity.com/paper/2308.14280/) (2023)
- [Using Machine Learning to Detect Fraudulent SMSs in Chichewa](https://mlsanity.com/paper/2502.16947/) (2025)
- [On Multilingual Encoder Language Model Compression for Low-Resource Languages](https://mlsanity.com/paper/2505.16956/) (2025)
- [From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle](https://mlsanity.com/paper/2510.27369/) (2025)
- [Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language](https://mlsanity.com/paper/2602.18964/) (2026)
- [YoNER: A New Yor`ub'a Multi-domain Named Entity Recognition Dataset](https://mlsanity.com/paper/2604.05624/) (2026)
- [Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe](https://mlsanity.com/paper/2604.12477/) (2026)
- [When Does Data Augmentation Help? Evaluating LLM and Back-Translation Methods for Hausa and Fongbe NLP](https://mlsanity.com/paper/2604.12540/) (2026)
- [A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development](https://mlsanity.com/paper/2605.22828/) (2026)
- [From Speech to Text Corpora: Evaluating ASR-Based Data Acquisition for Low-Resource Fongbe and Hausa](https://mlsanity.com/paper/2606.22274/) (2026)

## Abstract (arXiv)

In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the UD (universal dependencies) guidelines. We conducted extensive POS baseline experiments using conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in UD. Evaluating on the MasakhaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with cross-lingual parameter-efficient fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems more effective for POS tagging in unseen languages.