English-Twi Parallel Corpus for Machine Translation
Abstract
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.
Keywords
Cite
@article{arxiv.2103.15625,
title = {English-Twi Parallel Corpus for Machine Translation},
author = {Paul Azunre and Salomey Osei and Salomey Addo and Lawrence Asamoah Adu-Gyamfi and Stephen Moore and Bernard Adabankah and Bernard Opoku and Clara Asare-Nyarko and Samuel Nyarko and Cynthia Amoaba and Esther Dansoa Appiah and Felix Akwerh and Richard Nii Lante Lawson and Joel Budu and Emmanuel Debrah and Nana Boateng and Wisdom Ofori and Edwin Buabeng-Munkoh and Franklin Adjei and Isaac Kojo Essel Ampomah and Joseph Otoo and Reindorf Borkor and Standylove Birago Mensah and Lucien Mensah and Mark Amoako Marcel and Anokye Acheampong Amponsah and James Ben Hayfron-Acquah},
journal= {arXiv preprint arXiv:2103.15625},
year = {2021}
}
Comments
9 pages paper, Accepted at African NLP workshop @EACL 2021