  Language Modelling has been a central part of Natural Language Processing for
a very long time and in the past few years LSTM-based language models have been
the go-to method for commercial language modeling. Recently, it has been shown
that when looking at language modelling from a matrix factorization point of
view, the final Softmax layer limits the expressiveness of the model, by
putting an upper bound on the rank of the resulting matrix. Additionally, a new
family of neural networks based called NeuralODEs, has been introduced as a
continuous alternative to Residual Networks. Moreover, it has been shown that
there is a connection between these models and Normalizing Flows. In this work
we propose a new family of language models based on NeuralODEs and the
continuous analogue of Normalizing Flows and manage to improve on some of the
baselines.
