This paper presents our latest investigations of the jointly trained maximum entropy and recurrent neural network language models for Code-Switching speech. First, we explore extensively the integration of part-of-speech tags and language identifier information in recurrent neural network language models for CodeSwitching. Second, the importance of the maximum entropy model is demonstrated along with a various of experimental results. Finally, we propose to adapt the recurrent neural network language model to different Code-Switching behaviors and use them to generate artificial Code-Switching text data.
REF proposed to adapt the recurrent neural network language model to different code-switching behaviors and even use them to generate artificial code-switching text data.
Exploration of the Impact of Maximum Entropy in Recurrent Neural Network Language Models for Code-Switching Speech
CodeSwitch@EMNLP
Computer Science