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 describe básicamente los componentes de un aerodeslizador, y el sensor que podría ser utilizado para el control de navegación.  Es un estudio más bien teórico que no presenta evidencias de su construcción.  No queda claro si la propuesta ya fue publicada por el inserto en pie de página.  Se deben corregir varios errores de tipo gramatical; Ejemplos: Primer párrafo introducción ”con el término” y no “el término” C. Sistema de propulsión … a los cuales se acoplará Texto figura 2 debe incorporarse al pie de la figura Figura 3 sin título Figura 6 después de la 3? Penúltimo párrafo hoja 4 (“con estés….”) Enumerar ecuaciones.
[EX A]: es

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

[EX Q]: The paper describes a novel technique to improve the efficiency of computation graphs in deep learning frameworks. An impressive speedup can be observed in their implementation within TensorFlow. The content is presented with sufficient clarity, although some more graphical illustrations could be useful. This work is relevant in order to achieve highest performance in neural network training.   Pros:  - significant speed improvements through dynamic batching - source code provided   Cons:  - the effect on a large real-world (ASR, SMT) would allow the reader to put the improvements better into context - presentation/vizualisation can be improved
[EX A]:
en