  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.
