Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin
Pin-Jie Lin Muhammed Saeed Ernie Chang Merel Scholman
Developing effective spoken language processing systems for low-resource
languages poses several challenges due to the lack of parallel data and limited
resources for fine-tuning models. In this work, we target on improving upon
both text classification and translation of Nigerian Pidgin (Naija) by
collecting a large-scale parallel English-Pidgin corpus and further propose a
framework of cross-lingual adaptive training that includes both continual and
task adaptive training so as to adapt a base pre-trained model to low-resource
languages. Our studies show that English pre-trained language models serve as a
stronger prior than multilingual language models on English-Pidgin tasks with
up to 2.38 BLEU improvements; and demonstrate that augmenting orthographic data
and using task adaptive training with back-translation can have a significant
impact on model performance.