Improving Neural Language Models with a Continuous Cache
We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented networks.
The authors present a simple method to affix a cache to neural language models, which provides in effect a copying mechanism from recently used words. Unlike much related work in neural networks with copying mechanisms, this mechanism need not be trained with long-term backpropagation, which makes it efficient and scalable to much larger cache sizes. They demonstrate good improvements on language modeling by adding this cache to RNN baselines. The main contribution of this paper is the observation that simply using the hidden states h_i as keys for words x_i, and h_t as the query vector, naturally gives a lookup mechanism that works fine without tuning by backprop. This is a simple observation and might already exist as folk knowledge among some people, but it has nice implications for scalability and the experiments are convincing. The basic idea of repurposing locally-learned representations for large-scale attention where backprop would normally be prohibitively expensive is an interesting one, and could probably be used to improve other types of memory networks. My main criticism of this work is its simplicity and incrementality when compared to previously existing literature. As a simple modification of existing NLP models, but with good empirical success, simplicity and practicality, it is probably more suitable for an NLP-specific conference. However, I think that approaches that distill recent work into a simple, efficient, applicable form should be rewarded and that this tool will be useful to a large enough portion of the ICLR community to recommend its publication.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Improving Neural Language Models with a Continuous Cache
We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented networks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors present a simple method to affix a cache to neural language models, which provides in effect a copying mechanism from recently used words. Unlike much related work in neural networks with copying mechanisms, this mechanism need not be trained with long-term backpropagation, which makes it efficient and scalable to much larger cache sizes. They demonstrate good improvements on language modeling by adding this cache to RNN baselines. The main contribution of this paper is the observation that simply using the hidden states h_i as keys for words x_i, and h_t as the query vector, naturally gives a lookup mechanism that works fine without tuning by backprop. This is a simple observation and might already exist as folk knowledge among some people, but it has nice implications for scalability and the experiments are convincing. The basic idea of repurposing locally-learned representations for large-scale attention where backprop would normally be prohibitively expensive is an interesting one, and could probably be used to improve other types of memory networks. My main criticism of this work is its simplicity and incrementality when compared to previously existing literature. As a simple modification of existing NLP models, but with good empirical success, simplicity and practicality, it is probably more suitable for an NLP-specific conference. However, I think that approaches that distill recent work into a simple, efficient, applicable form should be rewarded and that this tool will be useful to a large enough portion of the ICLR community to recommend its publication.
assistant
The authors present a simple method to affix a cache to neural language models, which provides in effect a copying mechanism from recently used words. Unlike much related work in neural networks with copying mechanisms, this mechanism need not be trained with long-term backpropagation, which makes it efficient and scalable to much larger cache sizes. They demonstrate good improvements on language modeling by adding this cache to RNN baselines. The main contribution of this paper is the observation that simply using the hidden states h_i as keys for words x_i, and h_t as the query vector, naturally gives a lookup mechanism that works fine without tuning by backprop. This is a simple observation and might already exist as folk knowledge among some people, but it has nice implications for scalability and the experiments are convincing. The basic idea of repurposing locally-learned representations for large-scale attention where backprop would normally be prohibitively expensive is an interesting one, and could probably be used to improve other types of memory networks. My main criticism of this work is its simplicity and incrementality when compared to previously existing literature. As a simple modification of existing NLP models, but with good empirical success, simplicity and practicality, it is probably more suitable for an NLP-specific conference. However, I think that approaches that distill recent work into a simple, efficient, applicable form should be rewarded and that this tool will be useful to a large enough portion of the ICLR community to recommend its publication.