Sentence Encoding with Tree-Constrained Relation Networks
The meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variants of relation networks (RNs) which represent a set of objects (for us, words forming a sentence) in terms of representations of pairs of objects. We propose two extensions to the basic RN model for natural language. First, building on the intuition that not all word pairs are equally informative about the meaning of a sentence, we use constraints based on both supervised and unsupervised dependency syntax to control which relations influence the representation. Second, since higher-order relations are poorly captured by a sum of pairwise relations, we use a recurrent extension of RNs to propagate information so as to form representations of higher order relations. Experiments on sentence classification, sentence pair classification, and machine translation reveal that, while basic RNs are only modestly effective for sentence representation, recurrent RNs with latent syntax are a reliably powerful representational device.
 The main idea is to incorporate linguistic-based constrains in the form of dependency trees into different variations of relation networks. In general, the paper is well written and organized, the presented problem is well motivated, and the approach is very strait forward. The experimental setting is comprehensive, and the results are indeed competitive in a wide range of tasks. I think that using linguistic knowledge to improve Neural networks performance is very promising field, I think that you could get a much more substantial gains when applying your method in less resource-rich setups (maybe using some small subset of training for the SNLI and question duplication datasets). It seems that your method relies heavily on previous works (RN, RNN-RN, latent dependency trees ,intra-sentence attention), can you please state clearly what your contribution is? does your model has any advantages over current state-of-the-art methods? edit: I*m still not convinced about this article novelty, I really like the overall idea but it seems that this kind of contribution is better suited for short paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Sentence Encoding with Tree-Constrained Relation Networks
The meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variants of relation networks (RNs) which represent a set of objects (for us, words forming a sentence) in terms of representations of pairs of objects. We propose two extensions to the basic RN model for natural language. First, building on the intuition that not all word pairs are equally informative about the meaning of a sentence, we use constraints based on both supervised and unsupervised dependency syntax to control which relations influence the representation. Second, since higher-order relations are poorly captured by a sum of pairwise relations, we use a recurrent extension of RNs to propagate information so as to form representations of higher order relations. Experiments on sentence classification, sentence pair classification, and machine translation reveal that, while basic RNs are only modestly effective for sentence representation, recurrent RNs with latent syntax are a reliably powerful representational device.
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 idea is to incorporate linguistic-based constrains in the form of dependency trees into different variations of relation networks. In general, the paper is well written and organized, the presented problem is well motivated, and the approach is very strait forward. The experimental setting is comprehensive, and the results are indeed competitive in a wide range of tasks. I think that using linguistic knowledge to improve Neural networks performance is very promising field, I think that you could get a much more substantial gains when applying your method in less resource-rich setups (maybe using some small subset of training for the SNLI and question duplication datasets). It seems that your method relies heavily on previous works (RN, RNN-RN, latent dependency trees ,intra-sentence attention), can you please state clearly what your contribution is? does your model has any advantages over current state-of-the-art methods? edit: I*m still not convinced about this article novelty, I really like the overall idea but it seems that this kind of contribution is better suited for short paper.
assistant
 The main idea is to incorporate linguistic-based constrains in the form of dependency trees into different variations of relation networks. In general, the paper is well written and organized, the presented problem is well motivated, and the approach is very strait forward. The experimental setting is comprehensive, and the results are indeed competitive in a wide range of tasks. I think that using linguistic knowledge to improve Neural networks performance is very promising field, I think that you could get a much more substantial gains when applying your method in less resource-rich setups (maybe using some small subset of training for the SNLI and question duplication datasets). It seems that your method relies heavily on previous works (RN, RNN-RN, latent dependency trees ,intra-sentence attention), can you please state clearly what your contribution is? does your model has any advantages over current state-of-the-art methods? edit: I*m still not convinced about this article novelty, I really like the overall idea but it seems that this kind of contribution is better suited for short paper.