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.

本研究提供一份用于英语和 Akuapem Twi 的平行机器翻译训练语料库，其中包含 25,421 组句子对，主要使用 Transformer 为基础的翻译器生成初始的 Akuapem Twi 翻译，并通过由母语为 Akuapem Twi 的人验证和校对以消除翻译错误，同时提供了更高质量的 697 个众包句子，可用于下游自然语言处理任务的评估集。此研究通过 fine-tune transformer 翻译模型在该训练语料库上训练，并在众包测试集上进行性能基准测试，推荐这一数据集用于 Akuapem Twi 机器翻译的进一步训练和评估。