Recurrent neural network language models in the context of under-resourced South African languages

Over the past five years neural network models have been successful across a range of computational linguistic tasks. However, these triumphs have been concentrated in languages with significant resources such as large datasets. Thus, many languages, which are commonly referred to as under-resourced languages, have received little attention and have yet to benefit from recent advances. This investigation aims to evaluate the implications of recent advances in neural network language modelling techniques for under-resourced South African languages. Rudimentary, single layered recurrent neural networks (RNN) were used to model four South African text corpora. The accuracy of these models were compared directly to legacy approaches. A suite of hybrid models was then tested. Across all four datasets, neural networks led to overall better performing language models either directly or as part of a hybrid model. A short examination of punctuation marks in text data revealed that performance metrics for language models are greatly overestimated when punctuation marks have not been excluded. The investigation concludes by appraising the sensitivity of RNN language models (RNNLMs) to the size of the datasets by artificially constraining the datasets and evaluating the accuracy of the models. It is recommended that future research endeavours within this domain are directed towards evaluating more sophisticated RNNLMs as well as measuring their impact on application focused tasks such as speech recognition and machine translation