Towards Neural Machine Translation for Edoid Languages
It addresses language inequality and exclusion for millions of L1 speakers of indigenous Nigerian languages, though it is incremental as it applies existing methods to new data.
This work explores the feasibility of Neural Machine Translation for the Edoid language family in Nigeria, training baseline models on the JW300 dataset for four languages and achieving initial results, with all resources open-sourced for future research.
Many Nigerian languages have relinquished their previous prestige and purpose in modern society to English and Nigerian Pidgin. For the millions of L1 speakers of indigenous languages, there are inequalities that manifest themselves as unequal access to information, communications, health care, security as well as attenuated participation in political and civic life. To minimize exclusion and promote socio-linguistic and economic empowerment, this work explores the feasibility of Neural Machine Translation (NMT) for the Edoid language family of Southern Nigeria. Using the new JW300 public dataset, we trained and evaluated baseline translation models for four widely spoken languages in this group: Èdó, Ésán, Urhobo and Isoko. Trained models, code and datasets have been open-sourced to advance future research efforts on Edoid language technology.