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arXiv · 2103.15845

Text Normalization for Low-Resource Languages of Africa

Andrew Zupon·Evan Crew·Sandy Ritchie
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Abstract

Training data for machine learning models can come from many different sources, which can be of dubious quality. For resource-rich languages like English, there is a lot of data available, so we can afford to throw out the dubious data. For low-resource languages where there is much less data available, we can't necessarily afford to throw out the dubious data, in case we end up with a training set which is too small to train a model. In this study, we examine the effects of text normalization and data set quality for a set of low-resource languages of Africa -- Afrikaans, Amharic, Hausa, Igbo, Malagasy, Somali, Swahili, and Zulu. We describe our text normalizer which we built in the Pynini framework, a Python library for finite state transducers, and our experiments in training language models for African languages using the Natural Language Toolkit (NLTK), an open-source Python library for NLP.

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Andrew Zupon, Evan Crew, Sandy Ritchie. 2021-03-29. Text Normalization for Low-Resource Languages of Africa. https://arxiv.org/abs/2103.15845

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