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:
El artículo es un mero anuncio del trabajo de investigación que será realizado en una etapa posterior. Escasamente muestra el resumen del marco teórico, con sólo algunas referencias y un insuficiente estudio del estado del arte sobre el tema en cuestión. Por consiguiente no hay resultados en base a los cuales establecer juicios. El título del artículo no guarda relación a lo presentado en el cuerpo del informe. Presenta una gran cantidad de errores de redacción, ortográficos y gramaticales, figuras ilegibles, referencia a figuras inexistentes, título de una tabla en otra columna, etc.

Ex Output:
es


Ex Input:
The paper presents an approach for tackling the instability problem that is present in generative adversarial networks. The general idea is to allow the generator to "peek ahead" at how the discriminator will evolve its decision boundary over-time with the premise that this information should prevent the generator from collapsing to produce only samples from a single mode of the data distribution.  This is a very well written paper that clearly motivates its attack on an important open issue. The experiments are well carried out and strongly support the presented idea. The pursued approach is substantially more elegant than current existing "hacks" that are commonly used to make GANs work in practice. I however have three main issues that let me partly doubt the success of the method. If these can be resolved this paper is a clear candidate for acceptance.  1) I am not entirely convinced that the same effect cannot be obtained by the following procedure: simply train the discriminator for an extended number of K steps when updating the generator (say a number equivalent to the unrolling steps used in the current experiments) then, after the generator was updated undo the K updates to the discriminator and do 1 new update step instead. I only briefly glanced at your response to Reviewer2 which seems to imply you now tried something similar to this setup by stopping gradient flow at an appropriate point (although I think this is not exactly equivalent). 2) I tried to reproduce the simple MNIST example but using a fully connected network instead of an RNN generator without much success. Even when unrolling the discriminator for 30-40 steps the generator still engages in mode seeking behavior or does not train at all. This could either be because of a bug in my implementation or because of some peculiarities of the RNN generator or because I did not use batch normalization anywhere. If it is one of the latter two this would entail a dependence of the proposed approach on specific forms of the discriminator and generator and should be discussed. My code can be found here

Ex Output:
en


Ex Input:
This paper motivates the combination of autoregressive models with Variational Auto-Encoders and how to control the amount the amount of information stored in the latent code. The authors provide state-of-the-art results on MNIST, OMNIGLOT and Caltech-101. I find that the insights provided in the paper, e.g. with respect to the effect of having a more powerful decoder on learning the latent code, the bit-back coding, and the lossy decoding are well-written but are not novel. The difference between an auto-regressive prior and the inverse auto-regressive posterior is new and interesting though. The model presented combines the recent technique of PixelRNN/PixelCNN and Variational Auto-Encoders with Inverse Auto-Regressive Flows, which enables the authors to obtain state-of-the-art results on MNIST, OMNIGLOT and Caltech-101. Given the insights provided in the paper, the authors are also able to control the amount of information contained in the latent code to an extent. This paper gather several insight on Variational Auto-Encoders scattered through several publications in a well-written way. From these, the authors are able to obtain state-of-the-art models on small complexity datasets. Larger scale experiments will be necessary.

Ex Output:

en


