Venue: ICLR 2017 conference

Reviews:

final_score: rating: null
review_content: I am impressed by the results compared to the simplicity of the model.

You mention that the difference between your update gates and the softmax gating in previous work is that it gives local sigmoid attention computed only on the current memory. However when you unfold the recursion in eqn 3, multiplication of z_t or (1-z_t) values throughout time will act like weights to a weighted average across all memory (summing up to 1). Is that not very similar to a softmax gating, just that it uses a different function to compute the attention weights? And uses the same parameters that the softmax would use? I am not sure if that is a significant difference. Please clarify if I'm missing anything here.
review_title: Clarification on local vs global attention

final_score: rating: 7: Good paper, accept
review_content: The paper proposes a simplistic recurrent architecture that has a very small temporal dependence in its definition (which, in turn, allows it to be parallelized across the temporal axis). Paper is clear to follow, the architecture has appropriate motivation and the experiments seem to be very thorough and over an appropriate range of synthetic and real datasets. Results are very impressive on its own especially considering the simplicity of the model. Activation and gate visualizations are informative. Overall, the work is sufficient as a conference contribution.

I appreciate the effort by authors in responding to questions and incorporating revisions.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: This work introduces a new RNN model and shows how it can be used for QA in bAbI-like settings.  I think the model is simple and interesting, and the results are strong.  I would like the authors to also show results training on all tasks simultaneously, but other than that, I think it is an interesting model and it is nicely described and tested. 
review_title: null

final_score: rating: null
review_content: 1. The paper mentioned that 'reduces the original query to an easier-to-answer query as it observes each trigger (context sentence) through time', I do not get the point why the model could have this ability. 
2. The model is similar to strong-typed RNN with question as another input in ICML 2016, you also should cite this paper -- "Strongly-Typed Recurrent Neural Network".
review_title: Clarification on query-reduction

final_score: rating: 7: Good paper, accept
review_content: The paper proposed a simple and effective model for QA. The paper is easy to read and result is impressive on the synthetic and real dataset. The one question is the paper is called query-reduction, but there is no place to show this reduction explicitly.
review_title: null

final_score: rating: null
review_content: Do you train one model for all of the tasks, or is it the same architecture for each task, but a different model?
review_title: all tasks simultaneous?

Meta-review: The paper proposes a simple recurrent architecture for question-answering, achieving good performance on several reasoning benchmarks. All three reviewers agree on the merit of the contribution.
Decision: Accept (Poster)