Memory Architectures in Recurrent Neural Network Language Models
We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models. Our experiments on the Penn Treebank and Wikitext-2 datasets show that stack-based memory architectures consistently achieve the best performance in terms of held out perplexity. We also propose a generalization to existing continuous stack models (Joulin & Mikolov,2015; Grefenstette et al., 2015) to allow a variable number of pop operations more naturally that further improves performance. We further evaluate these language models in terms of their ability to capture non-local syntactic dependencies on a subject-verb agreement dataset (Linzen et al., 2016) and establish new state of the art results using memory augmented language models. Our results demonstrate the value of stack-structured memory for explaining the distribution of words in natural language, in line with linguistic theories claiming a context-free backbone for natural language.
 The main contribution of this paper are: (a) a proposed extension to continuous stack model to allow multiple pop operation, (b) on a language model task, they demonstrate that their model gives better perplexity than comparable LSTM and attention model, and (c) on a syntactic task (non-local subject-verb agreement), again, they demonstrate better performance than comparable LSTM and attention model. Additionally, the paper provides a nice introduction to the topic and casts the current models into three categories -- the sequential memory access, the random memory access and the stack memory access models. Their analysis in section (3.4) using the Venn diagram and illustrative figures in (3), (4) and (5) provide useful insight into the performance of the model.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Memory Architectures in Recurrent Neural Network Language Models
We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models. Our experiments on the Penn Treebank and Wikitext-2 datasets show that stack-based memory architectures consistently achieve the best performance in terms of held out perplexity. We also propose a generalization to existing continuous stack models (Joulin & Mikolov,2015; Grefenstette et al., 2015) to allow a variable number of pop operations more naturally that further improves performance. We further evaluate these language models in terms of their ability to capture non-local syntactic dependencies on a subject-verb agreement dataset (Linzen et al., 2016) and establish new state of the art results using memory augmented language models. Our results demonstrate the value of stack-structured memory for explaining the distribution of words in natural language, in line with linguistic theories claiming a context-free backbone for natural language.
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 main contribution of this paper are: (a) a proposed extension to continuous stack model to allow multiple pop operation, (b) on a language model task, they demonstrate that their model gives better perplexity than comparable LSTM and attention model, and (c) on a syntactic task (non-local subject-verb agreement), again, they demonstrate better performance than comparable LSTM and attention model. Additionally, the paper provides a nice introduction to the topic and casts the current models into three categories -- the sequential memory access, the random memory access and the stack memory access models. Their analysis in section (3.4) using the Venn diagram and illustrative figures in (3), (4) and (5) provide useful insight into the performance of the model.
assistant
 The main contribution of this paper are: (a) a proposed extension to continuous stack model to allow multiple pop operation, (b) on a language model task, they demonstrate that their model gives better perplexity than comparable LSTM and attention model, and (c) on a syntactic task (non-local subject-verb agreement), again, they demonstrate better performance than comparable LSTM and attention model. Additionally, the paper provides a nice introduction to the topic and casts the current models into three categories -- the sequential memory access, the random memory access and the stack memory access models. Their analysis in section (3.4) using the Venn diagram and illustrative figures in (3), (4) and (5) provide useful insight into the performance of the model.