Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper proposes to combine the distributed and symbolic execution for natural language queries. Based on the finding that the symbolic executor's column selection generally aligns with the field attention of the distributed enquirer, the authors incorporate the symbolic executor to the loss of the distributed enquirer by augmenting a field attention cross entropy loss into the original loss. This information is also used in pre-train the policy for the REINFORCE algorithm. The experiments show by combining the distributed and symbolic execution this way, the model achieve better performance.

I like the idea of incorporating the symbolic executor model into the neural model via attention. Similar ideas have been proposed in other papers too (for example https://arxiv.org/pdf/1511.04586.pdf -- section 2.6) It would be nice if the authors can refer more to the related works.
review_title: official review

final_score: rating: 6: Marginally above acceptance threshold
review_content: * Summary: the paper proposes to combine a distributed enquirer with a symbolic executor for the task of question answering. The idea is simple: the distributed enquirer is used for the policy initialization for the symbolic executer which is trained using REINFORCE. The proposed method outperforms the baseline SEMPRE on a QA dataset. 

* Discussion: 
- The paper is quite difficult to read, not because of the idea is complicated. Several details (e.g. math symbols) about the distributed enquirer can be safely omitted. Figure 1 hardly helps. It seems like the authors tried to shorten a long paper by "copy and paste". 
- The experimental results are impressive. However, why didn't the authors choose Yin et al (2016b) as the baseline? Table 2e is unclear: are the results on the dev or test set? If they are on the dev set, I was surprised to see that the performance on the test set is even substantially higher than the dev set. If they are on the test set, I then have no idea why the accuracy 96.5 is not on table 2a. 

TL,DR; I think the idea and the experimental results are good enough, but the paper must be rewritten in a clearer way.  
review_title: official review

Decision: Accept