Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling

摘要

In this paper, we investigate the application of recurrent neural network language models (RNNLM) and factored language models (FLM) to the task of language modeling for Code-Switching speech. We present a way to integrate part-of-speech tags (POS) and language information (LID) into these models which leads to significant improvements in terms of perplexity. Furthermore, a comparison between RNNLMs and FLMs and a detailed analysis of perplexities on the different backoff levels are performed. Finally, we show that recurrent neural networks and factored language models can be combined using linear interpolation to achieve the best performance. The final combined language model provides 37.8% relative improvement in terms of perplexity on the SEAME development set and a relative improvement of 32.7% on the evaluation set compared to the traditional n-gram language model.