  We consider the task of word-level language modeling and study the
possibility of combining hidden-states-based short-term representations with
medium-term representations encoded in dynamical weights of a language model.
Our work extends recent experiments on language models with dynamically
evolving weights by casting the language modeling problem into an online
learning-to-learn framework in which a meta-learner is trained by
gradient-descent to continuously update a language model weights.
