Meta-Learning a Dynamical Language Model

Thomas Wolf, Julien Chaumond, Clement Delangue

Feb 12, 2018 (modified: Feb 12, 2018) ICLR 2018 Workshop Submission readers: everyone
  • Abstract: 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.
  • TL;DR: Language modeling with dynamical weights as an instance of continuous meta-learning
  • Keywords: language models, hierarchical representations, meta-learning, nonstationarity, catastrophic forgetting, recurrent neural network