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: This paper introduces a hierarchical clustering method using learned CNN features to build 'the tree of life'. The assumption is that the feature similarity indicates the distance in the tree. The authors tried three different ways to construct the tree: 1) approximation central point 2) minimum spanning tree and 3) multidimensional scaling based method. Out of them, MDS works the best. It is a nice application of using deep features. However, I lean toward rejecting the paper because the following reasons:  1) All experiments are conducted in very small scale. The experiments include 6 fish species, 11 canine species, 8 vehicle classes. There are no quantitative results, only by visualizing the generated tree versus the wordNet tree. Moreover, the assumption of using wordNet is not quite valid. WordNet is not designed for biology purpose and it might not reflect the true evolutionary relationship between species.  2) Limited technical novelty. Most parts of the pipeline are standard, e.g. use pretrained model for feature extraction, use previous methods to construct hierarchical clustering. I think the technical contribution of this paper is very limited.

Output: en


Input: Consider Input: -          De valor dada la relevancia de la temática en la actualidad.  -          Entrega antecedentes empíricos respecto de la intensidad de la relación de las variables en estudio.  -          Define claramente una vía para futuras investigaciones.

Output: es


Input: Consider Input: This paper proposes an alternative to Conditional Variational Auto-Encoders and Conditional MultiModal Auto-Encoders to perform inference of missing modalities in dataset with multiple modalities. The proposed approach is a Variational Auto-Encoder jointly on all the modalities  with additional KL divergence penalties between the approximate posterior given all the modalities and the approximate posterior given a subset of the modalities. The approach is named Joint Multimodal Variational Auto-Encoder. The authors make a connection between this approach and the Variation of Information. It is unclear why the authors chose the JMVAE approach instead of a more elegant Variation of Information approach. Another unaddressed issue is the scalability of the method. As far as I can tell (given that no code is provided and the specification of the encoder is missing), this approach requires a new encoder per subset of missing modalities. Right now this approach seems to scale since there are only two modalities. The fact that the estimating the log-likelihood log(p(x)) using multiple modalities provide a lower value than with just one in Table 1 is a bit odd. Could you explain that ? The comparison with between the representation learned by JMVAE and CVAE might be unfair given that the representation of CVAE is learned conditionally, on the label in the case of MNIST, and should therefore not consider the label in this representation. Intuitively, this representation could represent "style" as shown in (Kingma et al., 2014) in their conditional generation figure. For CelebA, comparing log-likelihood on models that use GANs is probably not significant since GAN does not optimizes log-likelihood.  Overall this is an interesting problem and there are also interesting ideas worth exploring further, but the execution of the paper requires more work.


Output: en