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]: • Los títulos no van con “.” • Resumen   o“El objetivo de este documento es intentar recolectar…” --> sugiero quitar la palabra intentar •Abstract   oRevisar la gramática ya que se encuentran falta de artículos y algunas frases mal redactadas por ejemplo:    -	The images emerging technologies -->  the images technology emerging    -	such as machine vision systems and scanning --> such as the machine vision systems and the scanning…    -	…The objective of this paper is to try to collect… --> The objective of this paper is to try to collect    -	 the state of the art processing for 3d point cloud processing, going from the filters to the segmentation --> the state of the art of  processing the 3d point for the cloud processing going from the filters to the segmentation   oTexto en general revisar la falta de acentos y las palabras duplicadas, entre otros:    -Introducción: máquinas    -Trabajo realizado:       •	…que en general, que --> segundo que sobra       •	Trabajo realizado   oSimplificación de puntos, la segunda vez que pone la referencia [3] sobra    - La primera vez que aparecen unas siglas se deben incluir su significado RANSAC?       •	Segmentación   o...Muchas investigaciones --> cuáles? Incluir referencia de ese conjunto de muchas investigaciones   oExiste la palabra convolución? En la RAE no   oPresen-tan --> presentan       • Conclusiones   oFormato de las conclusiones       •	Referencias   oCompletar la referencias: 2,3,4,5,6,7 y 11       •	El artículo se queda en una recopilación de los trabajos enfocados en filtrado y documentación de la nube de puntos, recomiendo ir más allá, esto es hacer un mapeo de los trabajos que mencionas, el primero de los de simplificación y el segundo de los de segmentación basado en las características, alcance, debilidades y oportunidades de mejora identificadas, esto para que realmente hagas una aportación con tu artículo
[A]: es


[Q]: - Strengths:  -- A well-motivated approach, with a clear description and solid results.  - Weaknesses:  -- Nothing substantial other than the comments below.   - General Discussion:  The paper describes a new method called attention-over-attention for reading comprehension. First layers of the network compute a vector for each query word and document word, resulting in a |Q|xK matrix for the query and a |D|xK for the document. Since the answer is a document word, an attention mechanism is used for assigning weights to each word, depending on their interaction with query words. In this work, the authors deepen a traditional attention mechanism by computing a weight for each query word through a separate attention and then using that to weight the main attention over document words. Evaluation is properly conducted on benchmark datasets, and various insights are presented through an analysis of the results as well as a comparison to prior work. I think this is a solid piece of work on an important problem, and the method is well-motivated and clearly described, so that researchers can easily reproduce results and apply the same techniques to other similar tasks.  - Other remarks:  -- p4, Equation 12: I am assuming i is iterating over training set and p(w) is referring to P(w|D,Q) in the previous equation? Please clarify to avoid confusion.  -- I am wondering whether you explored/discussed initializing word embeddings with existing vectors such as Google News or Glove? Is there a reason to believe the general-purpose word semantics would not be useful in this task?  -- p6 L589-592: It is not clear what the authors are referring to when they say 'letting the model explicitly learn weights between individual attentions'? Is this referring to their own architecture, more specifically the GRU output indirectly affecting how much attention will be applied to each query and document word? Clarifying that would be useful. Also, I think the improvement on validation is not 4.1, rather 4.0 (72.2-68.2).  -- p7 Table 5: why do you think the weight for local LM is relatively higher for the CN task while the benefit of adding it is less? Since you included the table, I think it'll be nice to provide some insights to the reader.  -- I would have liked to see the software released as part of this submission.  -- Typo p2 L162 right column: "is not that effective than expected" --> "is not as effective as expected"?  -- Typo p7 L689 right column: "appear much frequent" --> "appears more frequently"?  -- Typo p8 L719-721 left column: "the model is hard to" --> "it is hard for the model to"? & "hard to made" --> "hard to make"?
[A]: en


[Q]: This paper considers the problem of KB completion and proposes ITransF for this purpose. Unlike STransE that assigns each relation an independent matrix, this paper proposes to share the parameters between different relations. A model is proposed where a tensor D is constructed that contains various relational matrices as its slices and a selectional vector \alpha is used to select a subset of relevant relational matrix for composing a particular semantic relation. The paper then discuss a technique to make \alpha sparse. Experimental results on two standard benchmark datasets shows the superiority of ITransF over prior proposals.  The paper is overall well written and the experimental results are good. However, I have several concerns regarding this work that I hope the authors will answer in their response.  1. Just by arranging relational matrices in a tensor and selecting (or more appropriately considering a linearly weighted sum of the relational matrices) does not ensure any information sharing between different relational matrices. This would have been the case if you had performed some of a tensor decomposition and projected the different slices (relational matrices) into some common lower-dimensional core tensor. It is not clear why this approach was not taken despite the motivation to share information between different relational matrices. 2. The two requirements (a) to share information across different relational matrices and (b) make the attention vectors sparse are some what contradictory. If the attention vector is truly sparse and has many zeros then information will not flow into those slices during optimisation.  3. The authors spend a lot of space discussing techniques for computing sparse attention vectors. The authors mention in page 3 that \ell_1 regularisation did not work in their preliminary experiments. However, no experimental results are shown for \ell_1 regularisation nor they explain why \ell_1 is not suitable for this task. To this reviewer, it appears as an obvious baseline to try, especially given the ease of optimisation. You use \ell_0 instead and get into NP hard optimisations because of it. Then you propose a technique and a rather crude approximation in the end to solve it. All that trouble could be spared if \ell_1 was used. 4. The vector \alpha is performing a selection or a weighing over the slices of D. It is slightly misleading to call this as “attention” as it is a term used in NLP for a more different type of models (see attention model used in machine translation). 5. It is not clear why you need to initialise the optimisation by pre-trained embeddings from TransE. Why cannot you simply randomly initialise the embeddings as done in TransE and then update them? It is not fair to compare against TransE if you use TransE as your initial point.  Learning the association between semantic relations is an idea that has been used in related problems in NLP such as relational similarity measurement [Turney JAIR 2012] and relation adaptation [Bollegala et al. IJCAI 2011]. It would be good to put the current work with respect to such prior proposals in NLP for modelling inter-relational correlation/similarity.  Thanks for providing feedback. I have read it.
[A]:
en