text: This paper investigates meta-learning strategy for automated architecture search in the context of RNN.  To constraint the architecture search space, authors propose a DSL that specifies the RNN recurrent operations.  This DSL allows to explore RNN architectures using either random search or a reinforcement-learning strategy.  Candidate architectures are ranked using a TreeLSTM that tries to predict the architecture performances.  The top-k architectures are then evaluated by fully training them on a given task. Authors evaluate their approach on  PTB/Wikitext 2 language modeling and Multi30k/IWSLT'16  machine translation.  In both experiments, authors show that their approach obtains competitive results and can sometime outperforms RNN cells such as GRU/LSTM.  In the PTB experiment, their architecture however underperforms other LSTM variant in the literatures. - Quality/Clarity The paper is overall well written and pleasant to read. Few details can be clarified.  In particular how did you initialize the weight and bias for both the LSTM/GRU baselines and the found architectures?  Is there other works leveraging RNN that report results on the Multi30k/IWSLT datasets? You state in paragraph 3.2 that human experts can inject the previous best known architecture when training the ranking networks.  Did you use this in the experiments?  If yes, what was the impact of this online learning strategy on the final results?  - Originality The idea of using DSL + ranking for architecture search seems novel. - Significance Automated architecture search is a promising way to design new networks.  However, it is not clear why the proposed approach is not able to outperforms other LSTM-based architectures on the PTB task.  Could the problem arise from the DSL that constraint too much the search space ?  It would be nice to have other tasks that are commonly used as benchmark for RNN to see where this approach stand. In addition, authors propose both a DSL, a random and RL generator and a ranking function.  It would be nice to disentangle the contributions of the different components.  In particular, did the authors compare the random search vs the RL based generator or the performances of the RL-based generator when the ranking network is not used? Although authors do show that they outperform NAScell in one setting,  it would be nice to have an extended evaluation (using character level PTB for instance).