# EthioMT: Parallel Corpus for Low-resource Ethiopian Languages

arXiv: https://arxiv.org/abs/2403.19365 · HTML: https://mlsanity.com/paper/2403.19365/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go](https://mlsanity.com/paper/2203.08351/) (2022)
- [Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities](https://mlsanity.com/paper/1912.03457/) (2019)
- [Low Resource Neural Machine Translation: A Benchmark for Five African Languages](https://mlsanity.com/paper/2003.14402/) (2020)
- [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)
- [Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform](https://mlsanity.com/paper/2102.07539/) (2021)
- [Parallel Corpus for Indigenous Language Translation: Spanish-Mazatec and Spanish-Mixtec](https://mlsanity.com/paper/2305.17404/) (2023)
- [Evaluating Amharic Machine Translation](https://mlsanity.com/paper/2003.14386/) (2020)

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

- [The State of Large Language Models for African Languages: Progress and Challenges](https://mlsanity.com/paper/2506.02280/) (2025)

## Abstract (arXiv)

Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to be desired for low-resource languages. This is due to the smaller size of available parallel corpora in these languages, if such corpora are available at all. NLP in Ethiopian languages suffers from the same issues due to the unavailability of publicly accessible datasets for NLP tasks, including MT. To help the research community and foster research for Ethiopian languages, we introduce EthioMT -- a new parallel corpus for 15 languages. We also create a new benchmark by collecting a dataset for better-researched languages in Ethiopia. We evaluate the newly collected corpus and the benchmark dataset for 23 Ethiopian languages using transformer and fine-tuning approaches.