Crowdsourcing Parallel Corpus for English-Oromo Neural Machine Translation using Community Engagement Platform

TL;DR

This paper presents a neural machine translation system for English and Afaan Oromo, leveraging a novel crowdsourcing approach via a Community Engagement Platform to enrich the limited parallel corpus.

Contribution

It introduces a new crowdsourcing method using a Community Engagement Platform to collect parallel sentences, enhancing resource availability for low-resource language translation.

Findings

Achieved promising translation results with a 40k sentence pair corpus.

Collected 25% of the corpus through crowdsourcing via CEP.

Demonstrated the effectiveness of community engagement in resource-scarce language translation.

Abstract

Even though Afaan Oromo is the most widely spoken language in the Cushitic family by more than fifty million people in the Horn and East Africa, it is surprisingly resource-scarce from a technological point of view. The increasing amount of various useful documents written in English language brings to investigate the machine that can translate those documents and make it easily accessible for local language. The paper deals with implementing a translation of English to Afaan Oromo and vice versa using Neural Machine Translation. But the implementation is not very well explored due to the limited amount and diversity of the corpus. However, using a bilingual corpus of just over 40k sentence pairs we have collected, this study showed a promising result. About a quarter of this corpus is collected via Community Engagement Platform (CEP) that was implemented to enrich the parallel corpus through crowdsourcing translations.