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].

Example Input: This paper proposes to initialize the weights of a deep neural network layer-wise with a marginal Fisher analysis model, making use of potentially the similarity metric. Pros: There are a lot of experiments, albeit small datasets, that the authors tested their proposed method on. Cons: lacking baseline such as discriminatively trained convolutional network on standard dataset such as CIFAR-10. It is also unclear how costly in computation to compute the association matrix A in equation 4. This is an OK paper, where a new idea is proposed, and combined with other existing ideas such as greedy-layerwise stacking, dropout, and denoising auto-encoders. However, there have been many papers with similar ideas perhaps 3-5 years ago, e.g. SPCANet. Therefore, the main novelty is the use of marginal Fisher Analysis as a new layer. This would be ok, but the baselines to demonstrate that this approach works better is missing. In particular, I'd like to see a conv net or fully connected net trained from scratch with good initialization would do at these problems. To improve the paper, the authors should try to demonstrate without doubt that initializing layers with MFA is better than just random weight matrices.
Example Output: en

Example Input: Novedosa propuesta. Bien estructurada. Correctamente escrita. Solo sugiero: Explicar con mas detalle la funcion trapezoidal de la pagina 2. Redactar nuevamente el parrafo anterior a la funcion trapezoidal A(x). Agrandar el esquema de la figura 1. Explicar la arquitectura propuesta mostrada en la figura 2. Revisar referencia de Feng et al. que es la referencia 2 y en un parrafo de la columna 2 pagina 1 esta marcada como referencia 1. Articulo no esta en el formato pedido por Infonor, por favor cambiar a este formato.
Example Output: es

Example Input: this proposes a multi-view learning approach for learning representations for acoustic sequences. they investigate the use of bidirectional LSTM with contrastive losses. experiments show improvement over the previous work. although I have no expertise in speech processing, I am in favor of accepting this paper because of following contributions: - investigating the use of fairly known architecture on a new domain. - providing novel objectives specific to the domain - setting up new benchmarks designed for evaluating multi-view models I hope authors open-source their implementation so that people can replicate results, compare their work, and improve on this work.
Example Output:
en