1. Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin
- Author
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Lin, Pin-Jie, Saeed, Muhammed, Chang, Ernie, and Scholman, Merel
- Subjects
FOS: Computer and information sciences ,Computer Science - Computation and Language ,Computation and Language (cs.CL) - 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., Comment: To appear in INTERSPEECH 2023
- Published
- 2023
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