text: The authors have addressed the problem of translating natural language queries to SQL queries.  They proposed a deep neural network based solution which combines the attention based neural semantic parser and pointer networks.  They also released a new dataset WikiSQL for the problem.  The proposed method outperforms the existing semantic parsing baselines on WikiSQL dataset. Pros:1. The idea of using pointer networks for reducing search space of generated queries is interesting.  Also, using extrinsic evaluation of generated queries handles the possibility of paraphrasing SQL queries. 2. A new dataset for the problem. 3. The experiments report a significant boost in the performance compared to the baseline.  The ablation study is helpful for understanding the contribution of different component of the proposed method. Cons:1. It would have been better to see performance of the proposed method in other datasets (wherever possible).  This is my main concern about the paper. 2. Extrinsic evaluation can slow down the overall training.  Comparison of running times would have been helpful. 3. More details about training procedure (specifically for the RL part) would have been better.