English-Twi Parallel Corpus for Machine Translation

TL;DR

This paper introduces a new English-Twi parallel corpus with 25,421 sentence pairs, including a high-quality evaluation set, to improve machine translation and NLP tasks involving Twi language.

Contribution

The creation of a verified, high-quality parallel corpus for English and Akuapem Twi, along with benchmarks for translation models and a dedicated evaluation dataset.

Findings

Transformer-based model achieved improved translation accuracy.

High-quality dataset enhances training and evaluation of Twi translation models.

Corpus supports various NLP tasks beyond 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.