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Neural language models predict the next token using a latent representationof the immediate token history. Recently, various methods for augmenting neurallanguage models with an attention mechanism over a differentiable memory havebeen proposed. For predicting the next token, these models query informationfrom a memory of the recent history which can facilitate learning mid- andlong-range dependencies. However, conventional attention mechanisms used inmemory-augmented neural language models produce a single output vector per timestep. This vector is used both for predicting the next token as well as for thekey and value of a differentiable memory of a token history. In this paper, wepropose a neural language model with a key-value attention mechanism thatoutputs separate representations for the key and value of a differentiablememory, as well as for encoding the next-word distribution. This modeloutperforms existing memory-augmented neural language models on two corpora.Yet, we found that our method mainly utilizes a memory of the five most recentoutput representations. This led to the unexpected main finding that a muchsimpler model based only on the concatenation of recent output representationsfrom previous time steps is on par with more sophisticated memory-augmentedneural language models.

Frustratingly Short Attention Spans in Neural Language Modeling