Recurrent neural network language model adaptation with curriculum learning

Our second set of experiments addresses limited-data within-domain adaptation, i.e., adapting an existing model trained on a large set of data using a smaller amount of data from the target sub-domain. Under this challenge, data from the target sub-domain is not available at the time when the language model is trained, but rather becomes available little by little over time. We demonstrate that the implicit interpolation carried out by applying curriculum learning methods to rnnlms outperforms conventional interpolation and has the potential to make more of less adaptation data.