# Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2303.14406 · HTML: https://mlsanity.com/paper/2303.14406/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 89 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Low Resource Neural Machine Translation: A Benchmark for Five African Languages](https://mlsanity.com/paper/2003.14402/) (2020)
- [Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets](https://mlsanity.com/paper/2011.01154/) (2020)
- [An Amharic News Text classification Dataset](https://mlsanity.com/paper/2103.05639/) (2021)
- [Extended Parallel Corpus for Amharic-English Machine Translation](https://mlsanity.com/paper/2104.03543/) (2021)
- [An Exploration of Data Augmentation Techniques for Improving English to Tigrinya Translation](https://mlsanity.com/paper/2103.16789/) (2021)
- [ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer](https://mlsanity.com/paper/2207.00785/) (2022)
- [The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation](https://mlsanity.com/paper/2210.15224/) (2022)
- [Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform](https://mlsanity.com/paper/2102.07539/) (2021)
- [Machine Learning Approaches for Amharic Parts-of-speech Tagging](https://mlsanity.com/paper/2001.03324/) (2020)

## Built on by (most cited first, 11 of 11)

- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets](https://mlsanity.com/paper/2402.08015/) (2024)
- [EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation](https://mlsanity.com/paper/2403.13737/) (2024)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [Whispering in Amharic: Fine-tuning Whisper for Low-resource Language](https://mlsanity.com/paper/2503.18485/) (2025)
- [Natural Language Processing for Tigrinya: Current State and Future Directions](https://mlsanity.com/paper/2507.17974/) (2025)
- [Amharic LLaMA and LLaVA: Multimodal LLMs for Low Resource Languages](https://mlsanity.com/paper/2403.06354/) (2024)
- [The Rise of AfricaNLP: A Survey of Contributions, Contributors, Community Impact, and Bibliometric Analysis](https://mlsanity.com/paper/2509.25477/) (2025)
- [Evaluation Sheet for Deep Research: A Use Case for Academic Survey Writing](https://mlsanity.com/paper/2510.01283/) (2025)
- [Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research](https://mlsanity.com/paper/2601.09716/) (2025)
- [AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning](https://mlsanity.com/paper/2602.09914/) (2026)

## Abstract (arXiv)

This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia. Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for various NLP tasks in these languages. This repository can be updated periodically with contributions from other researchers. Our objective is to identify research gaps and disseminate the information to NLP researchers interested in Ethiopian languages and encourage future research in this domain.