In this work, we explore mechanisms that can significantly improve upon such a baseline when applied to codeswitched text.
we develop two such mechanisms: • We alter the structure of an RNN unit to include separate components that focus on each • We propose using same-source pretrainingi.e., pretraining the model using data sampled from a generative model which is itself trained on the given training data -before training the model on the same training data (see Section 3).