English-Twi Parallel Corpus for Machine Translation
This provides a resource for machine translation and NLP tasks in Akuapem Twi, addressing a gap for this low-resource language, but it is incremental as it applies existing methods to new data.
The authors tackled the lack of parallel data for English-Akuapem Twi machine translation by creating a corpus of 25,421 sentence pairs, verified by native speakers, and reported benchmarks from fine-tuning a transformer model on this dataset.
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.