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].

[Q]: Some of the key details in this paper are very poorly explained or not even explained at all. The model sounds interesting and there may be something good here, but it should not be published in it's current form.   Specific comments:  The description of the R_l,pi convolutions in Section 2.1 was unclear. Specifically, I wasn't confident that I understood what the labels pi represented.  The description of the SAEN structure in section 2.2 was worded poorly. My understanding, based on Equation 1, is that the 'shift' operation is simply a summation of the representations of the member objects, and that the 'aggregate' operation simply concatenates the representations from multiple relations.  In the 'shift' step, it seems more appropriate to average over the object's member's representations h_j, rather than sum over them.  The compression technique presented in Section 2.3 requires that multiple objects at a level have the same representation. Why would this ever occur, given that the representations are real valued and high-dimensional? The text is unintelligible: "two objects are equivalent if they are made by same sets of parts for all the pi-parameterizations of the R_l,pi decomposition relation."   The 'ego graph patterns' in Figure 1 and 'Ego Graph  Neural Network' used in the experiments are never explained in the text, and no references are given. Because of this, I cannot comment on the quality of the experiments.
[A]: en


[Q]: - Strengths: 1) an interesting task, 2) the paper is very clearly written, easy to follow, 3) the created data set may be useful for other researchers, 4) a detailed analysis of the performance of the model.  - Weaknesses: 1) no method adapted from related work for a result comparison 2) some explanations about the uniqueness of the task and discussion on limitations of previous research for solving this problem can be added to emphasize the research contributions further.   - General Discussion: The paper presents supervised and weakly supervised models for frame classification in tweets. Predicate rules are generated exploiting language-based and Twitter behavior-based signals, which are then supplied to the probabilistic soft logic framework to build classification models. 17 political frames are classified in tweets in a multi-label classification task. The experimental results demonstrate the benefit of the predicates created using the behavior-based signals. Please find my more specific comments below:  The paper should have a discussion on how frame classification differs from stance classification. Are they both under the same umbrella but with different levels of granularity?  The paper will benefit from adding a brief discussion on how exactly the transition from long congressional speech to short tweets adds to the challenges of the task. For example, does past research rely on any specific cross-sentential features that do not apply to tweets? Consider adapting the method of a frame classification work on congressional speech (or a stance classification work on any text) to the extent possible due to its limitations on Twitter data, to compare with the results of this work.  It seems “weakly supervised” and “unsupervised” – these two terms have been interchangeably used in the paper (if this is not the case, please clarify in author response). I believe "weakly supervised" is the more technically correct terminology under the setup of this work that should be used consistently throughout. The initial unlabeled data may not have been labeled by human annotators, but the classification does use weak or noisy labels of some sort, and the keywords do come from experts. The presented method does not use completely unsupervised data as traditional unsupervised methods such as clustering, topic models or word embeddings would.    The calculated Kappa may not be a straightforward reflection of the difficulty of frame classification for tweets (lines: 252-253), viewing it as a proof is a rather strong claim. The Kappa here merely represents the annotation difficulty/disagreement. Many factors can contribute to a low value  such as poorly written annotation guidelines, selection of a biased annotator, lack of annotator training etc. (on top of any difficulty of frame classification for tweets by human annotators, which the authors actually intend to relate to). 73.4% Cohen’s Kappa is strong enough for this task, in my opinion, to rely on the annotated labels.   Eq (1) (lines: 375-377) will ignore any contextual information (such as negation or conditional/hypothetical statements impacting the contributing word) when calculating similarity of a frame and a tweet. Will this have any effect on the frame prediction model? Did the authors consider using models that can determine similarity with larger text units such as perhaps using skip thought vectors or vector compositionality methods?    An ideal set up would exclude the annotated data from calculating statistics used to select the top N bi/tri-grams (line: 397 mentions entire tweets data set has been used), otherwise statistics from any test fold (or labeled data in the weakly supervised setup) still leaks into the selection process. I do not think this would have made any difference in the current selection of the bi/tri-grams or results as the size of the unlabeled data is much larger, but would have constituted a cleaner experimental setup.    Please add precision and recall results in Table 4.   Minor: please double check any rules for footnote placements concerning placement before or after the punctuation.
[A]: en


[Q]: RESUMEN. El artículo busca mostrar las aplicaciones de Sistemas Inmune Biológico en la computación. Para ello, el artículo comienza describiendo la teoría sobre el Sistema Inmune Biológico de los vertebrados, el cual define, además de explicar su funcionamiento. A continuación describe los modelos biológicos como revisión teórica de la materia. Finalmente describe las características de los sistemas inmunes artificiales y detalla los distintos campos de aplicación.  Evaluación General. El tema del artículo es sumamente interesante, y poco veces visto (al menos por mi). Tal cual postula el artículo, hace una revisión bibliográfica de la teoría y entrega una descripción sobre la variedad de aplicaciones que tiene este campo de estudio. Logra resumir estos conceptos que pueden servir efectivamente como marco de referencia dentro de la literaria. Sin embargo, el artículo posee algunas debilidades:   1) La gran debilidad del artículo es su estructura. El artículo no sigue el formato estándar del trabajo de un artículo científico. Acá presento algunos ejemplos. a) En la introducción no se presenta la estructura que sigue el artículo, ni una idea general de lo que quieren lograr los autores. b) No hay un lineamiento de las ideas de cada sección, ni una introducción por sección. Esto se traduce en que no existe una propuesta clara por parte de los autores y que lo intentan proponer se ve solamente en la última página.   2) Otra debilidad es que no establece ninguna forma de evaluar y comparar las distintas aplicaciones de la materia con el fin de conectarlas con la teoría y presentar una contribución del artículo. Por esto, no cumple con lo establecido en su abstract: " . . . para establecer lineamientos en la aplicación de estas técnicas en las diferentes áreas del conocimiento."  3) Otro problema es que no establece ningún tipo de conclusión ni de las implicancias que tiene el artículo, el cual termina con la descripción de una aplicación en optimización combinatoria.  Otros comentarios. Problemas menores de redacción: a) "...y las moléculas pertenecientes a antígenos externos (no propias),  que en últimas son las que constituyen una amenaza para el organismo..." Se entiende que falta una palabra luego de “últimas”. b) "describe características básicas de una respuesta inmunológica" Esta frase no establece una definición. c) "células innatas inmunes especializadas células dendríticas" Aquí también faltan palabras.
[A]:
es