Finding ReMO (Related Memory Object): A Simple neural architecture for Text based Reasoning
Memory Network based models have shown a remarkable progress on the task of relational reasoning. Recently, a simpler yet powerful neural network module called Relation Network (RN) has been introduced. Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence limiting its application to tasks with a large-scaled memory. We introduce Related Memory Network, an end-to-end neural network architecture exploiting both memory network and relation network structures. We follow memory network*s four components while each component operates similar to the relation network without taking a pair of objects. As a result, our model is as simple as RN but the computational complexity is reduced to linear time. It achieves the state-of-the-art results in jointly trained bAbI-10k story-based question answering and bAbI dialog dataset.
 This paper proposes an alternative to the relation network architecture whose computational complexity is linear in the number of objects present in the input. The model achieves good results on bAbI compared to memory networks and the relation network model. From what I understood, it works by computing a weighted average of sentence representations in the input story where the attention weights are the output of an MLP whose input is just a sentence and question (not two sentences and a question). This average is then fed to a softmax layer for answer prediction. I found it difficult to understand how the model is related to relation networks, since it no longer scores every combination of objects (or, in the case of bAbI, sentences), which is the fundamental idea behind relation networks. Why is the approach not evaluated on CLEVR, in which the interaction between two objects is perhaps more critical (and was the main result of the original relation networks paper)? The fact that the model works well on bAbI despite its simplicity is interesting, but it feels like the paper is framed to suggest that object-object interactions are not necessary to explicitly model, which I can*t agree with based solely on bAbI experiments. I*d encourage the authors to do a more detailed experimental study with more tasks, but I can*t recommend this paper*s acceptance in its current form. other questions / comments: - "we use MLP to produce the attention weight without any extrinsic computation between the input sentence and the question." isn*t this statement false because the attention computation takes as input the concatenation of the question and sentence representation? - writing could be cleaned up for spelling / grammar (e.g., "last 70 stories" instead of "last 70 sentences"), currently the paper is very hard to read and it took me a while to understand the model
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Finding ReMO (Related Memory Object): A Simple neural architecture for Text based Reasoning
Memory Network based models have shown a remarkable progress on the task of relational reasoning. Recently, a simpler yet powerful neural network module called Relation Network (RN) has been introduced. Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence limiting its application to tasks with a large-scaled memory. We introduce Related Memory Network, an end-to-end neural network architecture exploiting both memory network and relation network structures. We follow memory network*s four components while each component operates similar to the relation network without taking a pair of objects. As a result, our model is as simple as RN but the computational complexity is reduced to linear time. It achieves the state-of-the-art results in jointly trained bAbI-10k story-based question answering and bAbI dialog dataset.
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>



 This paper proposes an alternative to the relation network architecture whose computational complexity is linear in the number of objects present in the input. The model achieves good results on bAbI compared to memory networks and the relation network model. From what I understood, it works by computing a weighted average of sentence representations in the input story where the attention weights are the output of an MLP whose input is just a sentence and question (not two sentences and a question). This average is then fed to a softmax layer for answer prediction. I found it difficult to understand how the model is related to relation networks, since it no longer scores every combination of objects (or, in the case of bAbI, sentences), which is the fundamental idea behind relation networks. Why is the approach not evaluated on CLEVR, in which the interaction between two objects is perhaps more critical (and was the main result of the original relation networks paper)? The fact that the model works well on bAbI despite its simplicity is interesting, but it feels like the paper is framed to suggest that object-object interactions are not necessary to explicitly model, which I can*t agree with based solely on bAbI experiments. I*d encourage the authors to do a more detailed experimental study with more tasks, but I can*t recommend this paper*s acceptance in its current form. other questions / comments: - "we use MLP to produce the attention weight without any extrinsic computation between the input sentence and the question." isn*t this statement false because the attention computation takes as input the concatenation of the question and sentence representation? - writing could be cleaned up for spelling / grammar (e.g., "last 70 stories" instead of "last 70 sentences"), currently the paper is very hard to read and it took me a while to understand the model
assistant
 This paper proposes an alternative to the relation network architecture whose computational complexity is linear in the number of objects present in the input. The model achieves good results on bAbI compared to memory networks and the relation network model. From what I understood, it works by computing a weighted average of sentence representations in the input story where the attention weights are the output of an MLP whose input is just a sentence and question (not two sentences and a question). This average is then fed to a softmax layer for answer prediction. I found it difficult to understand how the model is related to relation networks, since it no longer scores every combination of objects (or, in the case of bAbI, sentences), which is the fundamental idea behind relation networks. Why is the approach not evaluated on CLEVR, in which the interaction between two objects is perhaps more critical (and was the main result of the original relation networks paper)? The fact that the model works well on bAbI despite its simplicity is interesting, but it feels like the paper is framed to suggest that object-object interactions are not necessary to explicitly model, which I can*t agree with based solely on bAbI experiments. I*d encourage the authors to do a more detailed experimental study with more tasks, but I can*t recommend this paper*s acceptance in its current form. other questions / comments: - "we use MLP to produce the attention weight without any extrinsic computation between the input sentence and the question." isn*t this statement false because the attention computation takes as input the concatenation of the question and sentence representation? - writing could be cleaned up for spelling / grammar (e.g., "last 70 stories" instead of "last 70 sentences"), currently the paper is very hard to read and it took me a while to understand the model