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].

Ex Input:
ResNet and other architectures that use shortcuts have shown empirical success in several domains and therefore, studying the optimization for such architectures is very valuable. This paper is an attempt to address some of the properties of networks that use shortcuts. Some of the experiments in the paper are interesting. However, there are two main issues with the current paper:  1- linear vs non-linear: I think studying linear networks is valuable but we should be careful not to extend the results to networks with non-linear activations without enough evidence. This is especially true for Hessian as the Hessian of non-linear networks have very large condition number (see the ICLR submission "Singularity of Hessian in Deep Learning") even in cases where the optimization is not challenging. Therefore, I don't agree with the claims in the paper on non-linear networks. Moreover, one plot on MNIST is not enough to claim that non-linear networks behave similar to linear networks.  2- Hessian at zero initial point: The explanation of why we should be interested in Hessain at zero initial point is not acceptable. The zero initial point is not interesting because it is a very particular point that cannot tell us about the Hessian during optimization.

Ex Output:
en


Ex Input:
- Strengths: The idea of hard monotonic attention is new and substantially different from others.  - Weaknesses: The experiment results on morphological inflection generation is somewhat mixed. The proposed model is effective if the amount of training data is small (such as CELEX). It is also effective if the alignment is mostly monotonic and less context sensitive (such as Russian, German and Spanish).  - General Discussion:  The authors proposed a novel neural model for morphological inflection generation which uses "hard attention", character alignments separately obtained by using a Bayesian method for transliteration. It is substantially different from the previous state of the art neural model for the task which uses "soft attention", where character alignment and conversion are solved jointly in the probabilistic model.  The idea is novel and sound. The paper is clearly written. The experiment is comprehensive. The only concern is that the proposed method is not necessarily the state of the art in all conditions. It is suitable for the task with mostly monotonic alignment and with less context sensitive phenomena. The paper would be more convincing if it describe the practical merits of the proposed method, such as the ease of implementation and computational cost.

Ex Output:
en


Ex Input:
Authors present a parameterized variant of ELU and show that the proposed function helps to deal with vanishing gradients in deep networks in a way better than existing non-linearities. They present both a theoretical analysis and practical validation for presented approach.   Interesting observations on statistics of the PELU parameters are reported. Perhaps explanation for the observed evolution of parameters can help better understand the non-linearity. It is hard to evaluate the experimental validation presented given the difference in number of parameters compared to other approaches.

Ex Output:

en