TASK DEFINITION: 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].
PROBLEM: - Strengths:  This paper presents a 2 x 2 x 3 x 10 array of accuracy results based on systematically changing the parameters of embeddings models:  (context type, position sensitive, embedding model, task), accuracy  - context type ∈ {Linear, Syntactic} - position sensitive ∈ {True, False} - embedding model ∈ {Skip Gram, BOW, GLOVE} - task ∈ {Word Similarity, Analogies, POS, NER, Chunking, 5 text classific. tasks}  The aim of these experiments was to investigate the variation in performance as these parameters are changed. The goal of the study itself is interesting for the ACL community and similar papers have appeared before as workshop papers and have been well cited, such as Nayak et al.'s paper mentioned below.  - Weaknesses: Since this paper essentially presents the effect of systematically changing the  context types and position sensitivity, I will focus on the execution of the investigation and the analysis of the results, which I am afraid is not  satisfactory.  A) The lack of hyper-parameter tuning is worrisome. E.g.    - 395 Unless otherwise notes, the number of word embedding dimension is set to 500.    - 232 It still enlarges the context vocabulary about 5 times in practice.    - 385 Most hyper-parameters are the same as Levy et al' best configuration.    This is worrisome because lack of hyperparameter tuning makes it difficult to make statements like method A is better than method B. E.g. bound methods may perform better with a lower dimensionality than unbound models, since their effective context vocabulary size is larger.  B) The paper sometimes presents strange explanations for its results. E.g.    - 115 "Experimental results suggest that although it's hard to find any  universal insight, the characteristics of different contexts on different models are concluded according to specific tasks."     What does this sentence even mean?      - 580 Sequence labeling tasks tend to classify words with the same syntax  to the same category. The ignorance of syntax for word embeddings which  are learned by bound representation becomes beneficial.      These two sentences are contradictory, if a sequence labeling task    classified words with "same syntax" to same category then syntx becomes    a ver valuable feature. Bound representation's ignorance of syntax    should cause a drop in performance just like other tasks which does not    happen.  C) It is not enough to merely mention Lai et. al. 2016 who have also done a    systematic study of the word embeddings, and similarly the paper     "Evaluating Word Embeddings Using a Representative Suite of Practical    Tasks", Nayak, Angeli, Manning. appeared at the repeval workshop at     ACL 2016. should have been cited. I understand that the focus of Nayak    et al's paper is not exactly the same as this paper, however they    provide recommendations about hyperparameter tuning and experiment    design and even provide a web interface for automatically running    tagging experiments using neural networks instead of the "simple linear    classifiers" used in the current paper.  D) The paper uses a neural BOW words classifier for the text classification tasks    but a simple linear classifier for the sequence labeling tasks. What is    the justification for this choice of classifiers? Why not use a simple    neural classifier for the tagging tasks as well? I raise this point,    since the tagging task seems to be the only task where bound    representations are consistently beating the unbound representations,    which makes this task the odd one out.   - General Discussion: Finally, I will make one speculative suggestion to the authors regarding the analysis of the data. As I said earlier, this paper's main contribution is an analysis of the following table. (context type, position sensitive, embedding model, task, accuracy) So essentially there are 120 accuracy values that we want to explain in terms of the aspects of the model. It may be beneficial to perform factor analysis or some other pattern mining technique on this 120 sample data.

SOLUTION: en

PROBLEM: This paper introduces a mechanism for active learning with convolutional neural networks (CNNs). I would not go as far as the authors in calling these "deep", seeing that they seem to have only 2 hidden layers with only 20 filters each. The active learning criterion is a greedy selection scheme based on variational free energy and a series of approximations.  The paper is sometimes hard to read, due to (a) many grammatical errors and (b) sloppy notation in some places (e.g., on page 5, line 1, f is used but never introduced before). Overall, I give an accepting score, but a weak one because of the grammatical errors. If the paper is accepted, these should be fixed for the final version, optimally by a native speaker.   The paper's topic is interesting, and the paper appears to succeed in its goal of showing a proof of concept for active learning in CNNs (if only on toy datasets). I'm surprised by the new results on uncertainty sampling and curriculum learning the authors added: why do these methods both break for USPS? In particular, uncertainty sampling did very well (in fact, better than the authors' new method) on MNIST, but apparently horribly on USPS; some explanation for this would be useful.  I have one more question: why is it necessary to first sample a larger subset D \subset U, from which we select using active learning? Is this merely done for reasons of computational efficiency, or can it actually somehow improve results? (If so, it would be instrumental to see the worse results when this is not done.)

SOLUTION: en

PROBLEM: El documento ofrece una comparación respecto a la aplicación de algoritmos para la eliminación de ruidos impulsivos en dos tipos de arquitecturas de hardware: GPU y CPU.  El documento en general tiene una mala redacción, especialmente desde la página 4 en adelante.  El resumen del trabajo es adecuado, sin embargo hay que revisar su redacción, por ej. el uso de comas.  En la introducción se parte hablando de (i) procesamiento de imágenes, en medio (ii) se señala lo que se hace en el presente trabajo y luego se continua con (iii) una comparación entre GPU y CPU. Esta estructura no es adecuada, debería ser i-iii-ii, o simplemente mover iii a las secciones posteriores y dejar i-ii. Además de eso, no se habla mucho respecto al diseño experimental y a los resultados obtenidos, cosas que deben ser incluídas en al introducción.  La página 3 tiene un espacio en blanco grande que debe ser arreglado.  La sección "A. Fase de revisión" no tiene mucho valor como sección independiente, ya que menciona algo que es natural en un estudio de este tipo, que es la revisión bibliográfica. Se vería mucho mejor unirla con la sección siguiente (B).  la sección "D. Artefactos resultantes del proyecto" habla de "resultados de investigación", pero sólo se mencionan aspectos del software construidos más una planilla de resultados, sin embargo resultados directamente asociados a la investigación no se observan.  La página 5 tiene otro espacio en blanco.  En general, la presentación de resultados es pobre. No hay una tabla que indique claramente los resultados y los gráficos presentados no se ven bien. El análisis de los resultados es adecuado pero falta profundidad y relevancia respecto a que es lo que se quiere comparar exactamente en ambas arquitecturas. Las imágenes no son usadas ni explicadas en la comparación.  En resumen, el trabajo tiene buen destino pero requiere de un profundo trabajo correctivo para ser adecuado y científicamente aceptable.

SOLUTION:
es