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].
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Question: Comentarios generales: • Se recomienda revisar todo el escrito ya que hay palabras de más o palabras escritas de manera equivocada por ejemplo: imcompletas, ítemes (pag 3);  quedebe (pag 4), del de (pag 5)… etc • Revisar el espaciado de las palabras • Revisar la numeración de la bibliografía ya que tiene que ser listada en el orden en que ésta aparece. • Revisar que el texto sea del mismo tamaño  (ejemplo: final primera columna página 1)  Comentarios por secciones  II SCRUM YSPEM • Referenciar la parte dedicada a SCRUM así como la figura ya que no es propia de los autores si no tomada de alguien más  III LEVANTAMIENTO DE REQUERIMIENTOS UTILIZANDO SPEM • Revisar el concepto de stakeholder o referenciar de que referencia utilizó ese concepto, por ejemplo incluyo los conceptos de stakeholder para CMMI. “Parte interesada (stakeholder) Un grupo o individuo que se ve afectado por o es de alguna manera responsable del resultado de una empresa” “Parte interesada (stakeholder) Un grupo o individuo que se ve afectado por o es de alguna manera responsable del resultado de una empresa”  IV DESARROLLO DE SOFTWARE CON SCRUM • Título de Figura 5, puede mejorarse el título de la figura por ejemplo: modelo del proceso de desarrollo de software basado en SCRUM y utilizando SPEM para su modelado • Se recomienda reforzar la sección agregando referencias para los términos  V METRICAS PARA DEFINIR LA CALIDAD DEL DESARROLLO DEL SOFTWARE • Se recomienda agregar referencias cuando describe los conceptos de métricas  VI UN CASO PRÁCTICO • Se recomienda mejorar la descripción de la Tabla I (contenido de columnas y operación u operaciones llevadas a cabo para establecer las desviaciones estándar de peso y evaluación) • Hay una viñeta sin texto debajo de la Tabla 1 • Se recomienda apoyar la sección con una gráfica o tabla en la que se muestra un resumen de la medición de la calidad, para entender la medición “just in time” de la que habla en las conclusiones

Answer: es


Question: Authors propose a competitive learning architecture that learn different RNN predictors independently, akin to a committee of experts which are chosen with a hard switch at run-time. This work is applied to the task of predictive different driving behaviors from human drivers, and combines behaviors at test time, often switching behaviors within seconds. Prediction loss is lower than the similar but non-competitive architecture used as a baseline. It is not very clear how to interpret the results, what is the real impact of the model. If behaviors switch very often, can this really be seen as choosing the best driving mode for a given situation? Maybe the motivation needs to be rephrased a little to be more convincing? The competitive approach presented is interesting but not really novel, thus the impact of this paper for a conference such as ICLR may be limited.

Answer: en


Question: Authors' response well answered my questions. Thanks.  Evaluation not changed.  ###  This paper proposes a neural model for generating tree structure output from scratch. The model does 1) separate the recurrence between depths and siblings; 2) separate the topology and label generation, and outperforms previous methods on a benchmark IFTTT dataset. Compared to previous tree-decoding methods, the model avoids manually annotating subtrees with special tokens, and thus is a very good alternative to such problems. The paper does solid experiments on one synthetic dataset, and outperforms alternative methods on one real-world IFTTT dataset.   There are couple of interesting results in the paper that I believe is worth further investigation. Firstly, on the synthetic dataset, the precision drops rapidly with the number of nodes. Is it because that the vector representation of the sequential encoder fails to provide sufficient information of long sequences, such that the tree decoder can not do a good job? Or is it because that such tree decoder is not tolerant to the long sequence input, i.e., large tree structure? I believe that it is important to understand this before a better model can be developed. For example, if it is the fault of encoder, maybe an attention layer can be added, as in a seq-to-seq model, to preserve more information of the input sequence.   Moreover, besides only showing how the precision changes with the number of nodes in the tree, it might be interesting to investigate how it goes with 1) number of depths; 2) number of widths; 3) symmetricity; etc. Moreover, as greedy search is used in decoding, it might be interesting to see how it helps, if it does, to use beam-search in tree decoding.   On the IFTTT dataset, listing more statistics about this dataset might be helpful for better understanding the difficulty of this task. How deep are the trees? How large are the vocabularies on both language and program sides?  The paper is well written, except for minor typo as mentioned in my pre-review questions.   In general, I believe this is a solid paper, and more can be explored in this direction. So I tend to accept it.

Answer:
en