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:
This paper is a well written paper. This paper can be divided into 2 parts: 1.Adversary training on ImageNet  2.Empirical study of label leak, single/multiple step attack, transferability and importance of model capacity  For part [1], I don’t think training without clean example will not make reasonable ImageNet level model. Ian’s experiment in “Explaining and Harnessing Adversarial Examples” didn't use BatchNorm, which may be important for training large scale model. This part looks like an extension to Ian’s work with Inception-V3 model. I suggest to add an experiment of training without clean samples.  For part [2], The experiments cover most variables in adversary training, yet lack technical depth.  The depth, model capacity experiments can be explained by regularizer effect of adv training;  Label leaking is novel; In transferability experiment with FGSM, if we do careful observe on some special MNIST FGSM example, we can find augmentation effect on numbers, which makes grey part on image to make the number look more like the other numbers. Although this effect is hard to be observed with complex data such as CIFAR-10 or ImageNet, they may be related to the authors' observation "FGSM examples are most transferable".    In this part the authors raise many interesting problems or guess, but lack theoretical explanations.   Overall I think these empirical observations are useful for future work.

Ex Output:
en


Ex Input:
This paper proposes the graph convolutional networks, motivated from approximating graph convolutions.  In one propagation step, what the model does can be simplified as, first linearly transform the node representations for each node, and then multiply the transformed node representations with the normalized affinity matrix (with self-connections added), and then pass through nonlinearity.  This model is used for semi-supervised learning on graphs, and in the experiments it demonstrated quite impressive results compared to other baselines, outperforming them by a significant margin.  The evaluation of propagation model is also interesting, where different variants of the model and design decisions are evaluated and compared.  It is surprising that such a simple model works so much better than all the baselines.  Considering that the model used is just a two-layer model in most experiments, this is really surprising as a two-layer model is very local, and the output of a node can only be affected by nodes in a 2-hop neighborhood, and no longer range interactions can play any roles in this.  Since computation is quite efficient (sec. 6.3), I wonder if adding more layers helped anything or not.  Even though motivated from graph convolutions, when simplified as the paper suggests, the operations the model does are quite simple.  Compared to Duvenaud et al. 2015 and Li et al. 2016, the proposed method is simpler and does almost strictly less things.  So how would the proposed GCN compare against these methods?  Overall I think this model is simple, but the connection to graph convolutions is interesting, and the experiment results are quite good.  There are a few questions that still remain, but I feel this paper can be accepted.

Ex Output:
en


Ex Input:
El artículo propone y evalúa una aplicación desarrollada para representar de mejor forma los resultados de algoritmos de reglas de asociación. En general el artículo me parece bien escrito y estructurado. Tal vez, para mejorar, la estructura del documento agregaría una sección especial dedicada a la metodología y describiría ésta más detalladamente. Los autores evalúan cualitativamente el rendimiento de la interfaz en términos de la facilidad de interpretación de la información que entrega. Para ello exponen la información a un grupo de usuarios. El método de evaluación por usuarios es uno conocido y validado. No obstante, el artículo explica muy superficialmente el proceso de validación de usuarios usado. Por otra parte, los resultados de la evaluación presentados son sólo descriptivos. Podría haberse usado técnicas estadísticas de medición de consensos de intérpretes como el índice de Kappa u otros para analizar la calidad de la coincidencia entre los evaluadores. En síntesis, me parece un buen artículo con una idea bien elaborada pero que metodológicamente debería ser mejorado.

Ex Output:

es