Enhancing recurrent neural network-based language models by word tokenization

Different approaches have been used to estimate language models from a given corpus. Recently, researchers have used different neural network architectures to estimate the language models from a given corpus using unsupervised learning neural networks capabilities. Generally, neural networks have demonstrated success compared to conventional n-gram language models. With languages that have a rich morphological system and a huge number of vocabulary words, the major trade-off with neural network language models is the size of the network. This paper presents a recurrent neural network language model based on the tokenization of words into three parts: the prefix, the stem, and the suffix. The proposed model is tested with the English AMI speech recognition dataset and outperforms the baseline n-gram model, the basic recurrent neural network language models (RNNLM) and the GPU-based recurrent neural network language models (CUED-RNNLM) in perplexity and word error rate. The automatic spelling correction accuracy was enhanced by approximately 3.5% for Arabic language misspelling mistakes dataset.