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 Q]: El artículo aborda un tema de interés como lo es el manejo de grandes datos y su estructura en base a metadatos para su procesamiento en la era de la web semántica.  El artículo presenta serias deficiencias léxicas e incluso semánticas. La traducción del título y el abstract en inglés presentan errores graves. En el aspecto semántico, en la introducción mencionan como problema que las bases de datos relacionales no tengan problemas de adaptación. Como error léxico, escriben A un, en lugar de Aún. En forma errónea se utiliza el término Apropósito, en lugar de A propósito. Las conclusiones no permiten centrar una propuesta. Hay buena utilización de las referencias bibliográficas.
[EX A]: es

[EX Q]: SUMMARY  This paper discusses how data from a special type of low dimensional structure (monotonic chain) can be efficiently represented in terms of neural networks with two hidden layers.   PROS  Interesting, easy to follow view on some of the capabilities of neural networks, highlighting the dimensionality reduction aspect, and pointing at possible directions for further investigation.   CONS  The paper presents a construction illustrating certain structures that can be captured by a network, but it does not address the learning problem (although it presents experiments where such structures do emerge, more or less).   COMMENTS  It would be interesting to study the ramifications of the presented observations for the case of deep(er) networks.  Also, to study to what extent the proposed picture describes the totality of functions that are representable by the networks.   MINOR COMMENTS  - Figure 1 could be referenced first in the text.   - ``Color coded'' where the color codes what?  - Thank you for thinking about revising the points from my first questions. Note: Isometry on the manifold.  - On page 5, mention how the orthogonal projection on S_k is realized in the network.  - On page 6 ``divided into segments'' here `segments' is maybe not the best word.  - On page 6 ``The mean relative error is 0.98'' what is the baseline here, or what does this number mean?
[EX A]: en

[EX Q]: The paper introduces a number of ideas / heuristics for learning and interpreting deep generative models of text (tf-idf weighting, a combination of using an inference networks with direct optimization of the variational parameters, a method for inducing context-sensitive word embeddings). Generally, the last bit is the most novel, interesting and promising one, however, I agree with the reviewers that empirical evaluation of this technique does not seem sufficient.     Positive:  -- the ideas are sensible   -- the paper is reasonably well written and clear    Negative  -- most ideas are not so novel  -- the word embedding method requires extra analysis / evaluation, comparison to other methods for producing context-sensitive embeddings, etc
[EX A]:
en