Task: Language Identification
Input (Spanish, English): [Summary] This paper proposes a new way for knowledge base completion which highlights: 1) adopting an implicit shared memory, which makes no assumption about its structure and is completely learned during training; 2) modeling a multi-step search process that can decide when to terminate.  The experimental results on WN18 and FB15k seem pretty good. The authors also perform an analysis on a shortest path synthetic task, and demonstrate that this model is better than standard seq2seq.  The paper is well-written and it is easy to follow.  [Major comments] I actually do like the idea and am also impressed that this model can work well. The main concern is that this paper presents too little analysis about how it works and whether it is sensitive to the hyper-parameters, besides that only reporting a final model on WN18 and FB15k.  One key hyper-parameter I believe is the size of shared memory (using 64 for the experiments). I don’t think that this number should be fixed for all tasks, at least it should depend on the KB scale. Could you verify this in your experiments? Would it be even possible to make a memory structure with dynamic size?  The RL setting (stochastic search process) is also one highlight of the paper, but could you demonstrate that how much it does really help? I think it is necessary to compare to the following: remove the termination gate and fix the number of inference steps and see how well the model does? Also show how the performance varies on # of steps?  I appreciate your attempts on the shortest path synthetic task. However, I think it would be much better if you can demonstrate that under a real KB setting. You can still perform the shortest path analysis, but using KB  (e.g., Freebase) entities and relations.  [Minor comments] I am afraid that the output gate illustrated in Figure 1 is a bit confusing. There should be only one output, depending on when the search process is terminated.
Output (English): en