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]: - Una revisión interesante de la literatura - La propuesta de Taxonomía presentada  - El Caso de Estudio está tratado de manera muy débil. Además, los resultados no son expresados de una manera más rigurosa (por ejemplo, con porcentajes o gráficos). - De acuerdo a formato, poner como mínimo 5 palabras clave. - Debe cumplir con formato exigido, por ejemplo, no debe usar “et al” ni en el texto ni en las referencias, debe dejar espacios entre párrafos, etc. - La referencia 10 ¿es una tesis o un informe técnico? - Sugiero cambiar color de letra de Figura 2 para mayor legibilidad. - La Figura 2, ¿es realmente la Figura 1? - El texto en azul ¿es figura o tabla? (no aparece su identificación) - Indica que el listado de la Tabla 3 corresponde a “cada uno” de los niveles descritos, pero en la Tabla 3 no aparece el tercer nivel y debe decir Figura 3 y no Tabla 3 - En Figura 3 respectiva, debe cambiar “D. Selección de Proveedores” por “F. Selección de Proveedores”. También debe cambiar “E. Establecimiento del acuerdo” por “G. Establecimiento del contrato”. - Debe utilizar la palabra “requisito” o “requerimiento” pero no ambas. - En “C. Procedimientos de verificación” debe cambiar “2. Entorno de validación” por “2. Entorno de verificación” y “3. Procedimiento y criterios de validación” por “3. Procedimiento y criterios de verificación” - Cambiar en referencia 24 “Standarization” por “Standardization”
[EX A]: es

[EX Q]: This paper proposes to use RNN and reinforcement learning for solving combinatorial optimization problems. The use of pointer network is interesting as it enables generalization to arbitrary input size. The proposed method also "fintunes" on test examples with active search to achieve better performance.  The proposed method is theoretically interesting as it shows that RNN and RL can be combined to solve combinatorial optimization problems and achieve comparable performance to traditional heuristic based algorithms.  However, the lack of complexity comparison against baselines make it impossible to tell whether the proposed method has any practical value. The matter is further complicated by the fact that the proposed method runs on GPU while baselines run on CPU: it is hard to even come up with a meaningful unit of complexity. Money spent on hardware and electricity per instance may be a viable option.  Further more, the performance comparisons should be taken with a grain of salt as traditional heuristic based algorithms can often give better performance if allowed more computation, which is not controlled across algorithms.
[EX A]: en

[EX Q]: 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.
[EX A]:
en