# English-Twi Parallel Corpus for Machine Translation

arXiv: https://arxiv.org/abs/2103.15625 · HTML: https://mlsanity.com/paper/2103.15625/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)

## Built on by (most cited first, 7 of 7)

- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [SMOL: Professionally translated parallel data for 115 under-represented languages](https://mlsanity.com/paper/2502.12301/) (2025)
- [AFRIDOC-MT: Document-level MT Corpus for African Languages](https://mlsanity.com/paper/2501.06374/) (2025)
- [Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation](https://mlsanity.com/paper/2212.07571/) (2022)
- [Nsanku: Evaluating Zero-Shot Translation Performance of LLMs for Ghanaian Languages](https://mlsanity.com/paper/2605.04208/) (2026)

## Abstract (arXiv)

We present a parallel machine translation training corpus for English and Akuapem Twi of 25,421 sentence pairs. We used a transformer-based translator to generate initial translations in Akuapem Twi, which were later verified and corrected where necessary by native speakers to eliminate any occurrence of translationese. In addition, 697 higher quality crowd-sourced sentences are provided for use as an evaluation set for downstream Natural Language Processing (NLP) tasks. The typical use case for the larger human-verified dataset is for further training of machine translation models in Akuapem Twi. The higher quality 697 crowd-sourced dataset is recommended as a testing dataset for machine translation of English to Twi and Twi to English models. Furthermore, the Twi part of the crowd-sourced data may also be used for other tasks, such as representation learning, classification, etc. We fine-tune the transformer translation model on the training corpus and report benchmarks on the crowd-sourced test set.