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Recently, continuous cache models were proposed as extensions to recurrentneural network language models, to adapt their predictions to local changes inthe data distribution. These models only capture the local context, of up to afew thousands tokens. In this paper, we propose an extension of continuouscache models, which can scale to larger contexts. In particular, we use a largescale non-parametric memory component that stores all the hidden activationsseen in the past. We leverage recent advances in approximate nearest neighborsearch and quantization algorithms to store millions of representations whilesearching them efficiently. We conduct extensive experiments showing that ourapproach significantly improves the perplexity of pre-trained language modelson new distributions, and can scale efficiently to much larger contexts thanpreviously proposed local cache models.

Unbounded cache model for online language modeling with open vocabulary