What is a Neural Cache?

A Neural Cache, also known as a Continuous Cache, is a component of language modeling that stores previous hidden states in memory cells. These stored hidden states are then used as keys to retrieve their corresponding word, which is the next word in the sequence. Unlike other models, there is no transformation applied to the storage during the writing and reading process.

How Does a Neural Cache Work?

The Neural Cache utilizes the hidden representations of a language model to define a probability distribution over the words in the cache. At each time step, the cache stores pairs of a hidden representation, denoted as h, and the corresponding generated word, denoted as x. As a result, the vector h encodes the history of previously generated words.

The probability distribution over words stored in the Neural Cache is defined as:

p_cache(w | h(1...t), x(1...t)) ∝ Σ(i=1 to t-1)[1_set(w=x(i+1))] exp(θ(h>h(t)^Th(i)))

Here, the scalar θ is a parameter that controls the flatness of the distribution. When θ is equal to zero, the probability distribution over the history is uniform, and the model is equivalent to a unigram cache model.

Why is a Neural Cache Important?

A Neural Cache is essential in a language modeling task. In a traditional language model, the model must remember the entire context of previously generated text. This requirement can lead to challenges when it comes to memory and processing power, especially when generating long and complex text. A Neural Cache overcomes this issue by storing the hidden states of previous words in a memory cell, which is much more efficient than storing the entire context of previously generated text.

Furthermore, a Neural Cache has been shown to improve the perplexity of language models. Perplexity is a measure of how well a probability model can predict a sample. In language modeling, a lower perplexity score indicates that the model can better predict the next word in the sequence, leading to improved performance.

Applications of a Neural Cache

The Neural Cache has found applications in various natural language processing tasks, including text generation, machine translation, and speech recognition. In text generation, a Neural Cache can improve the quality of generated text by maintaining a better memory of previously generated text. In machine translation, a Neural Cache can improve the translation quality by storing the hidden representations of the source and target languages. Finally, in speech recognition, a Neural Cache can improve the efficiency of the decoding process by storing the hidden states of previously recognized words.

The Neural Cache is an important component of language modeling and has found applications in various natural language processing tasks. It overcomes memory and processing power limitations by storing the hidden states of previously generated text, leading to improved performance and efficiency.

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