Curriculum learning strategies are known to improve the accuracy, robustness and convergence rate for various language learning tasks using deep architectures (Bengio et al., 2009) . In this work, we design and experiment with several training curricula for two tasks -word-level language detection and language modeling -for code-switched text data. Our study shows that irrespective of the task or the underlying DNN architecture, the best curriculum for training the code-switched models is to first train a network with monolingual training instances, where each mini-batch has instances from both languages, and then train the resulting network on code-switched data.
REF adapted an effective curriculum learning by training a network with monolingual corpora of two languages, and subsequently trained on code-switched data.
Design for Code-switching : Experiments with Language Identification and Language Modeling with Deep Neural Networks