  Lack of text data has been the major issue on code-switching language
modeling. In this paper, we introduce multi-task learning based language model
which shares syntax representation of languages to leverage linguistic
information and tackle the low resource data issue. Our model jointly learns
both language modeling and Part-of-Speech tagging on code-switched utterances.
In this way, the model is able to identify the location of code-switching
points and improves the prediction of next word. Our approach outperforms
standard LSTM based language model, with an improvement of 9.7% and 7.4% in
perplexity on SEAME Phase I and Phase II dataset respectively.
