Meta-Learning a Dynamical Language Model

Thomas Wolf; Julien Chaumond; Clement Delangue

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arXiv · 1803.10631

Meta-Learning a Dynamical Language Model

Thomas Wolf·Julien Chaumond·Clement Delangue

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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.

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Thomas Wolf, Julien Chaumond, Clement Delangue. 2018-03-28. Meta-Learning a Dynamical Language Model. https://arxiv.org/abs/1803.10631

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