Task: Language Identification
Input (Spanish, English): This paper proposed to use RL and RNN to design the architecture of networks for specific tasks. The idea of the paper is quite promising and the experimental results on two datasets show that method is solid. The pros of the paper are: 1. The idea of using RNN to produce the description of the network and using RL to train the RNN is interesting and promising. 2. The generated architecture looks similar to what human designed, which shows that the human expertise and the generated network architectures are compatible.  The cons of the paper are: 1. The training time of the network is long, even with a lot of computing resources.  2. The experiments did not provide the generality of the generated architectures. It would be nice to see the performances of the generated architecture on other similar but different datasets, especially the generated sequential models.  Overall, I believe this is a nice paper. But it need more experiments to show its potential advantage over the human designed models.
Output (English): en