Paper:
Published as a conference paper at ICLR 2018 NATURAL LANGUAGE INFERENCE OVER INTERACTION SPACE
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space. We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information. One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus. It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI; ) dataset with respect to the strongest published system.

Review:
This paper proposes Densely Interactive Inference Network to solve recognizing textual entailment via extracting a semantic feature from interaction tensor end-to-end. Their results show that this model has better performance than others.
Even though the results of this paper is interesting, I have the problem with paper writing and motivation for their architecture:
- Paper pages are well beyond 8-page limits for ICLR. The paper should be 8-pages + References. This paper has 11 pages excluding the references.
-  The introduction text in the 2nd page doesn't have smooth flow and sometimes hard to follow.
-  In my view section, 3.1 is redundant and text in section 3.2 can be improved
-  Encoding layer in section 3.2 is really hard to follow in regards to equations and naming e.g p_{itr att} and why choose \alpha(a,b,w)? 
-  Encoding layer in section 3.2, there is no motivation why it needs to use fuse gate.
-  Feature Extraction Layer is very confusing again. What is FSDR or TSDR?
-  Why the paper uses Eq. 8? the intuition behind it?
-  One important thing which is missing in this paper, I didn't understand what is the motivation behind using each of these components? and how each of these components is selected?
- How long does it take to train this network? Since it needs to works with other models (GLOV+ char features + POS tagging,..), it requires lots of effort to set up this network.
Even though the paper outperforms others, it would be useful to the community by providing the motivation and intuition why each of these components was chosen. This is important especially for this paper because each layer of their architecture uses multiple components, i.e. embedding layer [Glov+ Character Features + Syntactical features].  In my view, having just good results are not enough and will not guarantee a publication in ICLR, the paper should be well-written and well-motivated in order to be useful for the future research and the other researchers.
In summary, I don't think the paper is ready yet and it needs significant revision.
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Comments after the rebuttal and revision :
I'd like thanks the authors for the revision and their answers. 
Here are my comments after reading the revised version and considering the rebuttal:
- It is fair to say that the paper presentation is much better now. That said I am still having issues with 11 pages.
- The authors imply on page 2, end of paragraph 5,  that this is the first work that shows attention weight contains rich semantic and previous works are used attention merely as a medium for alignment.  Referring to the some of the related works (cited in this paper), I am not sure this is a correct statement.
- The authors claim to introduce a new class of architectures for NLI and generability of for this problem. In my view, this is a very strong statement and unsupported in the paper, especially considering ablation studies (table 5). In order for the model to show the best performance, all these components should come together. I am not sure why this method can be considered a class of architecture and why not just a new model?
some other comments:
- In page 4, the citation is missing for highway networks
- Page 5, equation 1, the parenthesis should close after \hat{P}_j.
Since the new version has been improved, I have increased my review score.  However, I'm still not convinced that this paper would be a good fit at ICLR given novelty and contribution.