A Vietnamese Language Model Based on Recurrent Neural Network

Language modeling plays a critical role in many natural language processing (NLP) tasks such as text prediction, machine translation and speech recognition. Traditional statistical language models (e.g. n-gram models) can only offer words that have been seen before and can not capture long word context. Neural language model provides a promising solution to surpass this shortcoming of statistical language model. This paper investigates Recurrent Neural Networks (RNNs) language model for Vietnamese, at character and syllable-levels. Experiments were conducted on a large dataset of 24M syllables, constructed from 1,500 movie subtitles. The experimental results show that our RNN-based language models yield reasonable performance on the movie subtitle dataset. Concretely, our models outperform n-gram language models in term of perplexity score.