Official DeepMind DNC code is here: https://github.com/deepmind/dnc
They use "an unmodified version of the architecture as specified in the original paper", and it looks like they copy & pasted the core code.
This paper lacks some further explanation. Why do they use XGBoost for predicting whether some word is to be normalized? And why do they use DNC for the seq2seq model? I think a single shared model for both tasks might be a cleaner solution. E.g. an encoder which with output layer for the prediction and also this encoder is fed to the decoder. The motivation for DNC is also not too clear, although I can guess that they think this is too hard for a LSTM. But for DNC, to get the advantages out of it, it should support some time for doing internal calculations, which you could get by introducing internal computation steps. They don't do that. Also, in their results section, they do not compare to any other model, so it it not clear whether XGBoost is the best choice, and also not whether the DNC really helps here.