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].

Input: Consider Input: The authors proposed to use leaky rectified linear units replacing binary units in Gaussian RBM.  A sampling method was presented to train the leaky-ReLU RBM. In the experimental section, AIS estimated likelihood on Cifar10 and SVHN were reported.   It's interesting for trying different nonlinear hidden units for RBM. However, there are some concerns for the current work.  1. The author did not explain why the proposed sampling method (Alg. 2) is correct. And the additional computation cost (the inner loop and the projection) should be discussed.  2. The results (both the resulting likelihood and the generative samples) of Gaussian RBM are much worse than what we have experienced. It seems that the Gaussian RBM were not trained properly.  3. The representation learned from a good generative model often helps the classification task when there are fewer label samples. Gaussian RBM works well for texture synthesis tasks in which mixing is an important issue. The authors are encouraged to do more experiments in these two direction.

Output: en


Input: Consider Input: De alto perfil técnico, es un aporte a la temática.  Demasiado conciso, podría extenderse en el cómo (sobretodo desde el aporte fuzzy).

Output: es


Input: Consider Input: The paper introduces an extension of the entity grid model. A convolutional neural network is used to learn sequences of entity transitions indicating coherence, permitting better generalisation over longer sequences of entities than the direct estimates of transition probabilities in the original model.  This is a nice and well-written paper. Instead of proposing a fully neural approach, the authors build on existing work and just use a neural network to overcome specific issues in one step. This is a valid approach, but it would be useful to expand the comparison to the existing neural coherence model of Li and Hovy. The authors admit being surprised by the very low score the Li and Hovy model achieves on their task. This makes the reader wonder if there was an error in the experimental setup, if the other model's low performance is corpus-dependent and, if so, what results the model proposed in this paper would achieve on a corpus or task where the other model is more successful. A deeper investigation of these factors would strengthen the argument considerably.  In general the paper is very fluent and readable, but in many places definite articles are missing (e.g. on lines 92, 132, 174, 488, 490, 547, 674, 764 and probably more). I would suggest proofreading the paper specifically with article usage in mind. The expression "...limits the model to do X...", which is used repeatedly, sounds a bit unusual. Maybe "limits the model's capacity to do X" or "stops the model from doing X" would be clearer.  --------------  Final recommendation adjusted to 4 after considering the author response. I agree that objective difficulties running other people's software shouldn't be held against the present authors. The efforts made to test the Li and Hovy system, and the problems encountered in doing so, should be documented in the paper. I would also suggest that the authors try to reproduce the results of Li and Hovy on their original data sets as a sanity check (unless they have already done so), just to see if that works for them.


Output: en