Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin

• URL: http://arxiv.org/abs/2307.00382v1
• Date: Sat, 1 Jul 2023 16:47:36 GMT
• Title: Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin
• Authors: Pin-Jie Lin, Muhammed Saeed, Ernie Chang, Merel Scholman
• Abstract summary: We aim to improve upon both text classification and translation of Nigerian Pidgin (Naija) by collecting a large-scale parallel English-Pidgin corpus. 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.
• Score: 3.2039731457723604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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