Text Normalization for Low-Resource Languages of Africa

• URL: http://arxiv.org/abs/2103.15845v1
• Date: Mon, 29 Mar 2021 18:00:26 GMT
• Title: Text Normalization for Low-Resource Languages of Africa
• Authors: Andrew Zupon, Evan Crew, Sandy Ritchie
• Abstract summary: In this study, we examine the effects of text normalization and data set quality for a set of low-resource languages of Africa. 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.
• Score: 1.5766133856827325
• License: http://creativecommons.org/licenses/by/4.0/
• 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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