In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

[Q]: - Strengths: The idea to investigate the types of relations between lexical items is very interesting and challenging. The authors make a good argument why going beyond analogy testing makes sense. - Weaknesses: The paper does not justify or otherwise contextualize the choice of clustering for evaluation, rather than using a classification task, despite the fact that classification tasks are more straightforward to evaluate. No attempt is being made to explain the overall level of the results. How well would humans do on this task (given only the words, no context)? - General Discussion: I have read the authors' response.
[A]: en

[Q]: This work describes a gated attention-based recurrent neural network method for reading comprehension and question answering. This method employs a self-matching attention technique to counterbalance the limited context knowledge of gated attention-based recurrent neural networks when processing passages. Finally, authors use pointer networks with signals from the question attention-based vector to predict the beginning and ending of the answer. Experimental results with the SQuAD dataset offer state-of-the-art performance compared with several recent approaches. The paper is well-written, structured and explained. As far as I know, the mathematics look also good. In my opinion, this is a very interesting work which may be useful for the question answering community. I was wondering if the authors have plans to release the code of this approach. From that perspective, I miss a bit of information about the technology used for the implementation (theano, CUDA, CuDNN...), which may be useful for readers. I would appreciate if authors could perform a test of statistical significance of the results. That would highlight even more the quality of your results. Finally, I know that the space may be a constraint, but an evaluation including some additional dataset would validate more your work.
[A]: en

[Q]: The authors explore the idea of deep-learning a static analyzer. They do it with a toy programming language and a very simplified analysis problem -- just checking if all variables are initalized. While the idea is interesting and might be developped into a tool in the future, the toy task presented in this paper is too simple to warrant an ICLR submission. Just detecting whether a variable is initialized in a string is a toy algorihtmic task, similar to the ones solved in a number of paper in recent years by models such as the Neural Turing Machine, Stack RNNs, Neural GPU, or Differentiable Neural Computer. All these architectures perform almost perfectly on a number of algorithmic tasks, so it is highly probable that they would also solve this one. Unluckily, the authors only compare to much more basic models, such as HMMs. Since the code for many of the above-mentioned models is available online, a paper without these baselines is not ready for ICLR. Moreover, there is a risk that existing models already solve this problem very well, making the contribution unclear.
[A]:
en