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: Taking into account the loss in the binarization step through a proximal Newton algorithm is a nice idea. This is at least one approach to bringing in the missing loss in the binarization step, which has recently gone from a two step process of train and binarize to a single step simultaneous train/compress. Performance on a few small tasks show the benefit. It would be nice to see some results on substantial networks and tasks which really need compression on embedded systems (a point made in the introduction). Is it necessary to discuss exploding/vanishing gradients when the RNN experiments are carried out by an LSTM, and handled by the cell error carousel? We see the desire to tie into proposition 2, but not clear that the degradation we see in the binary connect is related. Adam is used in the LSTM optimization, was gradient clipping really needed, or is the degradation of binary connect simply related to capacity? For proposition 3.1, theorem 3.1 and proposition 3.2 put the pointers to proofs in appendix.
Example Output: en

Example Input: Resumen: El trabajo muestra un modelo para medir el valor y buscar su trazabilidad. Se propone la co-creacion de valor con el cliente como metodo de crear valor. Con el fin de analizar el aporte de la co-creacion se introduce el concepto de trazabilidad. Luego explica una serie de variables propuestas para medir el valor co-creado bajo un modelo conceptual junto con otro modelo que permite la trazabilidad de los distintos aportes a traves de enlaces. Finalmente se expone brevemente los resultados del modelo en un caso. Evaluacion: Implementar tipos de co-creacion es muy importante para las organizaciones actualmente. Es interesante como el articulo mezcla la co-creacion con la trazabilidad y propone maneras de medirla, presentando nuevas herramientas para la organizacion. Lamentablemente el articulo posee grandes debilidades: 1) El alcance puede que no sea apropiado para un congreso como INFONOR. 2) Los resultados son breves y debilmente ejemplificados, lo que dificulta la comprension de uso del modelo. 3) Podrian usarse mas ejemplos con fines de facilitar la lectura. 4) El formato del paper tiene problemas. Otros comentarios: Se encuentran problemas de redaccion: a)"Las organizaciones se comprometen con los consumidores en producir unos conceptos o unas ideas iniciales y utilizan los consumidores" Abuso en la palabra consumidor b)"se valido la el modelo de Arquetipos Sistemicos" pequeno error, claramente "la" esta demas
Example Output: es

Example Input: - Strengths: The idea of hard monotonic attention is new and substantially different from others. - Weaknesses: The experiment results on morphological inflection generation is somewhat mixed. The proposed model is effective if the amount of training data is small (such as CELEX). It is also effective if the alignment is mostly monotonic and less context sensitive (such as Russian, German and Spanish). - General Discussion: The authors proposed a novel neural model for morphological inflection generation which uses "hard attention", character alignments separately obtained by using a Bayesian method for transliteration. It is substantially different from the previous state of the art neural model for the task which uses "soft attention", where character alignment and conversion are solved jointly in the probabilistic model. The idea is novel and sound. The paper is clearly written. The experiment is comprehensive. The only concern is that the proposed method is not necessarily the state of the art in all conditions. It is suitable for the task with mostly monotonic alignment and with less context sensitive phenomena. The paper would be more convincing if it describe the practical merits of the proposed method, such as the ease of implementation and computational cost.
Example Output:
en