niryuu/sni-each-converted huggingface dataset:
The paper introduces a segmentation procedure for breast tissue images using SVM.  General comments:  a) Concerning the proposed approach: The segmentation method introduced only considers local color/gray value information. Perhaps introducing other features related to the membrane's local (e.g. the membrane has symmetry properties that can be exploited) or global (e.g. continuity, approximately circular shape) properties may improve the segmentation results.  Feature selection is also a key component to improve performance. Use a feature selection procedure.  b) Concerning the experimentation: You should describe the experiments with sufficient detail so others can replicate them: a) Make your data publicly available b) Explain how you have selected the classifier's parameters (parameter selection in an SVM is critical and may improve notably the performance).  You should join Figs 10 and 9 in a single plot so we can better see the differences.  You only provide qualitative segmentation results (Figs. 12, 14, 15), but quantitative results are also very important. Specially when it comes to comparisons. You should quantitatively compare your algorithm with other competing works in the literature.  c) Concerning paper organization: The paper is well written and clearly introduces the problem, describes the relevant literature and presents the solution. I have a few comments concerning the paper organization: a) Problem statement and literature review (intro and theoretical framework sections) are in my opinion a bit long, compared to other important sections, e.g. feature extraction and experimentation which are just one paragraph each. b) Although English is in general correct, there are some small mistakes that could be solved by having it read by a professional. c) There are two "proposed method" sections  Well written paper. Good literature review.  It only shows very preliminary results.
Output: en
La aplicación de sistemas SCADA para la minería es algo interesante pero el trabajo solo se limita a mencionar las herramientas móviles y no existe un punto de comparación claro entre ellas. Tampoco queda claro el aporte específico de estas herramientas en un proceso minero real.  Existen problemas de consistencia en el resumen y la introducción. Las conclusiones no son suficientemente convincentes, además existen algunos problemas de redacción.
Output: es
El artículo realiza una descripción general del modelo CMMI y de las metodologías ágiles, tomando como casos particulares Scrum y XP. Se realiza una discusión sobre la combinación de estos enfoques para la mejora de procesos, realizando un mapeo de las áreas de proceso REQM y PPQA con sus equivalentes en las metodologías ágiles.  El artículo realiza descripciones generales correctas de las metodologías, pero no contiene elementos de investigación novedosos que aporten al conocimiento en esta área. Para la comunidad técnica/científica de ingeniería de software resulta evidente que los enfoques tienen distintos puntos de partida, pero son combinables. Esto es especialmente cierto para expertos en mejora de proceso de software.  La revisión que se realiza sobre la investigación de la combinación de agilidad y CMMI no es sistemática. Por estado del arte no se entiende una descripción simple de cada enfoque, sino recoger la investigación más actualizada sobre este tema.  Si bien puede ser interesante realizar un mapeo entre áreas de proceso y prácticas ágiles, no se explica que pasos se van a seguir para realizar esto. Tampoco se realiza una validación de este mapeo o se explica cómo puede ser aplicado, por ejemplo por un consultor en mejora de procesos. No se aporta ninguna evidencia empírica de la aplicabilidad o utilidad del mapeo.
Output: es
Ese trabajo muestra el procedimiento propuesto para la Identificación automática de fracturas en imágenes de pozos. La metodología se ve novedosa e interesante. El artículo muestra bien la metodología y tiene una buena redacción. Se agregaron métricas para medir la efectividad del algoritmo.  Este trabajo fue presentado en INFONOR 2014 y la redacción actual es la misma, que si bien es cierto es buena, presenta los mismos pequeños errores que en el artículo anterior. Por lo tanto, debe volver a revisarlo.  Aunque ahora muestra métricas para medir el resultado no se indica qué tan efectivo es este sistema respecto al utilizado antes.  Debe mejorar las figuras 12 y 15, en donde muestras las métricas. La figura 2, izquierda no se distingue. Falta incluir una referencia a la figura 11 en el texto.
Output: es
This paper proposed an iterative query updating mechanism for cloze-style QA. The approach is novel and interesting and while it is only verified in the paper for two Cloze-style tasks (CBT and WDW), the concept of read/compose/write operations seem to be more general and can be potentially applied to other reasoning tasks beyond Cloze-style QA. Another advantage of the proposed model is to learn when to terminate the iteration by the so-called adaptive computation model, such that it avoids the issue of treating the number of iterations as another hyper-parameter, which is a common practice of iterative models/multi-hop reasoning in previous papers.  There are a couple places that this paper can improve. First, I would like to see the results from CNN/Daily Mail as well to have a more comprehensive comparison. Secondly, it will be useful to visualize the entire M^q sequence over time t (not just z or the query gating) to help understand better the query regression and if it is human interpretable.
Output: en
Sorry to pop up late, but I've been looking over ICLR accepted papers, and I noticed this one. Something very similar, by combining word and character information at the feature level using sigmoid gating, has been done before, see 
Output: en
Es un buen tema para el congreso. Presenta una buena propuesta de trabajo de vinculación  Está redactado como un abstract extendido, debe ser re-escrito para ser aceptado definitivamente para el congreso. No hay referencias y las que coloca al final son pobres. Por la presentación debería ser castigado
Output: es
Important problem, simple (in a positive way) idea, broad experimental evaluation; all reviewers recommend accepting the paper, and the AC agrees. Please incorporate any remaining reviewer feedback.
Output: en
This paper proposes a framework for evaluation of word embeddings based on data efficiency and simple supervised tasks. The main motivation is that word embeddings are generally used in a transfer learning setting, where evaluation is done based on how faster is to train a target model. The approach uses a set of simple tasks evaluated in a supervised fashion, including common benchmarks such as word similarity and word analogy. Experiments on a broad set of embeddings show that ranks tend to be task-specific and change according to the amount of training data used.  Strengths  - The transfer learning / data efficiency motivation is an interesting one, as it directly relates to the idea of using embeddings as a simple "semi-supervised" approach.  Weaknesses  - A good evaluation approach would be one that propagates to end tasks. Specifically, if the approach gives some rank R for a set of embeddings, I would like it to follow the same rank for an end task like text classification, parsing or machine translation. However, the approach is not assessed in this way so it is difficult to trust the technique is actually more useful than what is traditionally done. - The discussion about injective embeddings seems completely out-of-topic and does not seem to add to the paper's understanding. - The experimental section is very confusing. Section 3.7 points out that the analysis results in answers to questions as "is it worth fitting syntax specific embeddings even when supervised datset is large?" but I fail to understand where in the evaluation the conclusion was made. - Still in Section 3.7, the manuscript says "This hints, that purely unsupervised large scale pretraining might not be suitable for NLP applications". This is a very bold assumption and I again fail to understand how this can be concluded from the proposed evaluation approach. - All embeddings were obtained as off-the-shelf pretrained ones so there is no control over which corpora they were trained on. This limits the validity of the evaluation shown in the paper. - The manuscript needs proofreading, especially in terms of citing figures in the right places (why Figure 1, which is on page 3, is only cited in page 6?).  General Discussion  I think the paper starts with a very interesting motivation but it does not properly evaluate if their approach is good or not. As mentioned above, for any intrinsic evaluation approach I expect to see some study if the conclusions propagate to end tasks and this is not done in the paper. The lack of clarity and proofreading in the manuscript also hinders the understanding. In the future, I think the paper would vastly benefit from some extrinsic studies and a more controlled experimental setting (using the same corpora to train all embeddings, for instance). But in the current state I do not think it is a good addition to the conference.
Output: en
This is an interesting paper about quantized networks that work on temporal difference inputs.  The basic idea is that when a network has only to process differences then this is computational much more efficient specifically with natural video data since large parts of an image would be fairly constant so that the network only has to process the informative sections of the image (video stream). This is of course how the human visual system works, and it is hence of interest even beyond the core machine learning community.   As an aside, there is a strong community interested in event-based vision such as the group of Tobi Delbrück, and it might be interesting to connect to this community. This might even provide a reference for your comments on page 1.  I guess the biggest novel contribution is that a rounding network can be replaced by a sigma-delta network, but that the order of discretization and summation doe make some difference in the actual processing load. I think I followed the steps and  Most of my questions have already been answers in the pre-review period. My only question remaining is on page 3, “It should be noted that when we refer to “temporal differences”, we refer not to the change in the signal over time, but in the change between two inputs presented sequentially. The output of our network only depends on the value and order of inputs, not on the temporal spacing between them.”  This does not make sense to me. As I understand you just take the difference between two frames regardless if you call this temporal or not it is a change in one frame. So this statement rather confuses me and maybe should be dropped unless I do miss something here, in which case some more explanation would be necessary.  Figure 1 should be made bigger.  An improvement of the paper that I could think about is a better discussion of the relevance of the findings. Yes, you do show that your sigma-delta network save some operation compared to threshold, but is this difference essential for a specific task, or does your solution has relevance for neuroscience?
Output: en
El trabajo aborda el problema de la navegación autónoma de robots móviles mediante la definición de una arquitectura de control reactiva.  Los autores deben ser más claros en indicar por qué utilizar una arquitectura reactiva, en relación con otros enfoques disponibles.  Es necesario mejorar la legibilidad de las ecuaciones.  El trabajo es correcto y pertinente. El ámbito de la robótica móvil tiene múltiples aplicaciones.  Es necesario incorporar algunos lineamientos sobre trabajo futuro.
Output: es
Se trata de un artículo bastante particular que propone una extensión a los modelos de datos existentes considerando la idea de "sistema" (de objetos). Para esto se propone un lenguaje (LATIN) y un modelo de datos ¿universal?, MED, basado en un "volumen esférico" con dos polos "mayor" y "diario" basados en ideas contables. Este es, a mi juicio, el aspecto más crítico de la propuesta, ya que surge la duda, de si todo sistema debería calzar en este esquema, o estamos hablando de un conjunto particular de casos. Las ideas posteriores son interesante y, al menos, generan un quiebre con respecto a las ideas clásicas de la orientación a objetos. Cito la idea de sistema propuesta: "un sistema es un conjunto de clases enlazadas e inter-actuantes según un mismo método, el cual no es particular a cada clase sino emergente de todas ellas". Otras ideas asocian transacción con conjunto referencial, macros, tipos, instancias, ... lo que en términos globales constituyen un nuevo cuerpo de ideas. No habiendo en las referencias alguna cita a algún artículo anterior del autor que trate estas ideas, me parece que puede llegar a ser interesante escuchar esta propuesta y debatir al respecto. En términos de formalidad, el artículo no cumple cabalmente con el formato del congreso, y el número de páginas excede las ocho solicitadas.
Output: es
I'd like to thank the authors for their detailed response and clarifications.  This work proposes new training scheme for online sparse dictionary learning. The model assumes a non-stationary flow of the incoming data. The goal (and the challenge) is to learn a model in an online manner in a way that is capable of  adjusting to the new incoming data without forgetting how to represent previously seen data. The proposed approach deals with this problem by incorporating a mechanism for adding or deleting atoms in the dictionary. This procedure is inspired by the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus.   The paper has two main innovations over the baseline approach (Mairal et al): (i) “neuronal birth” which represents an adaptive way of increasing the number of atoms in the dictionary (ii) "neuronal death", which corresponds to removing “useless” dictionary atoms.  Neural death is implemented by including an group-sparsity regularization to the dictionary atoms themselves (the group corresponds to a column of the dictionary). This promotes to shrink to zero atoms that are not very useful, keeping controlled the increase of the dictionary size.  I believe that the strong side of the paper is its connections with the adult neurogenesis phenomenon, which is, in my opinion a very nice feature. The paper is very well written and easy to follow.  On the other hand, the overall technique is not very novel. Although not exactly equivalent, similar ideas have been explored. While the neural death is implemente elegantly with a sparsity-promoting regularization term, the neural birth is performed by relying on heuristics that measure how well the dictionary can represent new incoming data. Which depending on the "level" of non-stationarity in the incoming data (or presence of outliers) could be difficult to set. Still, having adaptive dictionary size is very interesting.  The authors could also cite some references in model selection literature. In particular, some ideas such as MDL have been used for automatically selecting the dictionary size (I believe this work does not address the online setting, but still its a relevant reference to have). For instance,  Ramirez, Ignacio, and Guillermo Sapiro. "An MDL framework for sparse coding and dictionary learning." IEEE Transactions on Signal Processing 60.6 (2012): 2913-2927.
Output: en
The idea of building a graph-based differentiable memory is very good. The proposed approach is quite complex, but it is likely to lead to future developments and extensions. The paper has been much improved since the original submission. The results could be strengthened, with more comparisons to existing results on bAbI and baselines on the experiments here. Exploring how it performs with less supervision, and different types of supervision, from entirely labeled graphs versus just node labels, would be valuable.
Output: en
This paper proposes a recurrent architecture for simultaneously predicting motion and action states of agents. The paper is well written, clear in its presentation and backed up by good experiments. They demonstrate that by forcing the network to predict motion has beneficial consequences on the classification of actions states, allowing more accurate classification with less training data. They also show how the information learned by the network is interpretable and organised in a hierarchy.  Weaknesses: - a critical discussion on the interplay between motion an behaviour that is needed to experience the benefits of their proposed model is missing from the paper. - moreover, a discussion on how this approach could scale to more challenging scenarios "involving animals" and visual input for instance and more general "behaviours" is also missing; The criticism here is pointed at the fact that the title/abstract claim general behaviour modelling, whilst the experiments are focused on two very specific and relatively simple scenarios, making the original claim a little bit far fetched unless its backed up by additional evidence. Using "Insects", or "fruit flies" would be more appropriate than "animals".
Output: en
Abstract: Needs to have a definition of ERP - can't assume the reader knows what this means.  Intro: Avoid 1 sentence paragraphs (page 1) The introduction is rather long - it seems to actually be two different sections: an introduction (1st four paragraphs) and 1+ page of background and hypothesis. Overall - at 1.5 pages the intro is rather long for a paper that is 4 pages total.  Methodology: I think there are a lot of assumptions in regards to what ERP are and how they work.  While the paper is a statistical study, it would have benefited with a context of an actual example.  The samples are from small to medium size companies (how many?) with 49 use case (how do these relate?).  Results: Discussion is too limited - it assumes that a reader is very familiar with the area, and that may not be the case.
Output: en
Overview: This work seems very promising, but I believe it should be compared with more baselines, and more precisely described and explained, from a signal processing point of view.  Pros: New descriptor Fast implementation  Cons: a) Lack of rigor b) Too long accordingly to the content c) The computational gain of the algorithm is not clear d) The work is not compared with its most obvious baseline: a scattering transform  I will detail each cons.  a) Section 1: The author  motivates the use of scattering transform because it defines a contraction of the space that relies on geometric features. " The nonlinearity used in the scattering network is the complex modulus which is piecewise linear." A real modulus is piecewise linear. A complex modulus has a shape of bell when interpreting C as R^2. Could you clarify? \Omega is not introduced.  Could you give a precise reference (page+paper) of this claim: “Higher order nonlinearity refers to |x|^2 instead of |x| as it is usually done in the scattering network.” ?  Section 2: The motivation of the non-linearity is not clear. First, this non-linearity might potentially increase a lot the variance of your architecture since it depends on higher moments(up to 4). I think a fair analysis would be to compute numerically the normalized variance (e.g. divided by the averaged l^2 norm), as a sanity check. Besides, one should prove that the energy is decreasing. It is not possible to argue that this architecture is similar to a scattering transform which has precise mathematical foundations and those results are required, since the setting is different.  Permutation is not a relevant variability.  The notion of sparsity during the whole paper sometimes refers to the number of 0 value, either the l^1 norm. Mathematically, a small value, even 10^-1000 is still a non 0 value.  Did you compute the graph of the figure 4 on the bird dataset? You might use a ratio instead for clarity.   The wavelet that is defined is not a morlet wavelet (
Output: en
- Strengths: The idea to investigate the types of relations between lexical items is very interesting and challenging. The authors make a good argument why going beyond analogy testing makes sense.    - Weaknesses: The paper does not justify or otherwise contextualize the choice of clustering for evaluation, rather than using a classification task, despite the fact that classification tasks are more straightforward to evaluate. No attempt is being made to explain the overall level of the results. How well would humans do on this task (given only the words, no context)?  - General Discussion:  I have read the authors' response.
Output: en
This paper presents several weakly supervised methods for developing NERs. The methods rely on some form of projection from English into another language. The overall approach is not new and the individual methods proposed are improvements of existing methods. For an ACL paper I would have expected more novel approaches.  One of the contributions of the paper is the data selection scheme. The formula used to calculate the quality score is quite straightforward and this is not a bad thing. However, it is unclear how the thresholds were calculated for Table 2. The paper says only that different thresholds were tried. Was this done on a development set? There is no mention of this in the paper. The evaluation results show clearly that data selection is very important, but one may not know how to tune the parameters for a new data set or a new language pair.   Another contribution of the paper is the combination of the outputs of the two systems developed in the paper. I tried hard to understand how it works, but the description provided is not clear.   The paper presents a number of variants for each of the methods proposed. Does it make sense to combine more than two weakly supervised systems? Did the authors try anything in this direction.  It would be good to know a bit more about the types of texts that are in the "in-house" dataset.
Output: en
- Strengths: This paper introduced a novel method to improve zero pronoun resolution performance.. The main contributions of this papers are: 1) proposed a simple method to automatically generate a large training set for zero pronoun resolution task; 2) adapted a two step learning process to transfer knowledge from large data set to the specific domain data; 3) differentiate unknown words using different tags. In general, the paper is well written. Experiments are thoroughly designed.   - Weaknesses:  But I have a few questions regarding finding the antecedent of a zero pronoun: 1. How will an antecedent be identified, when the prediction is a pronoun? The authors proposed a method by matching the head of noun phrases. It’s not clear how to handle the situation when the head word is not a pronoun. 2. What if the prediction is a noun that could not be found in the previous contents? 3. The system achieves great results on standard data set. I’m curious is it possible to evaluate the system in two steps? The first step is to evaluate the performance of the model prediction, i.e. to recover the dropped zero pronoun into a word; the second step is to evaluate how well the systems works on finding an antecedent.  I’m also curious why the authors decided to use attention-based neural network. A few sentences to provide the reasons would be helpful for other researchers.  A minor comment: In figure 2, should it be s1, s2 … instead of d1, d2 ….?   - General Discussion: Overall it is a great paper with innovative ideas and solid experiment setup.
Output: en
This paper studies neural models that can be applied to set-structured inputs and thus require permutation invariance or equivariance. After a first section that introduces necessary and sufficient conditions for permutation invariance/equivariance, the authors present experiments in supervised and semi-supervised learning on point-cloud data as well as cosmology data.    The reviewers agreed that this is a very promising line of work and acknowledged the effort of the authors to improve their paper after the initial discussion phase. However, they also agree that the work appears to be missing more convincing numerical experiments and insights on the choice of neural architectures in the class of permutation-covariant.     In light of these reviews, the AC invites their work to the workshop track.   Also, I would like to emphasize an aspect of this work that I think should be addressed in the subsequent revision.    As the authors rightfully show (thm 2.1), permutation equivariance puts very strong constraints in the class of 1-layer networks. This theorem, while rigorous, reflects a simple algebraic property of matrices that commute with permutation matrices. It is therefore not very surprising, and the resulting architecture relatively obvious. So much so that it already exists in the literature. In fact, it is a particular instance of the graph neural network model of Scarselli et al. '09 (
Output: en
It is an interesting idea to go after saddle points in the optimization with an SR1 update and a good start in experiments, but missing important comparisons to recent 2nd order optimizations such as Adam, other Hessian free methods (Martens 2012), Pearlmutter fast exact multiplication by the Hessian. From the mnist/cifar curves it is not really showing an advantage to AdaDelta/Nag (although this is stated), and much more experimentation is needed to make a claim about mini-batch insensitivity to performance, can you show error rates on a larger scale task?
Output: en
El artículo presenta una revisión bibliográfica sobre técnicas de elicitación de requisitos. Este trabajo hace énfasis en clasificar las técnicas de elicitación a partir del área de conocimiento de los estudios bibliográficos revisado y a partir de atributos como Eficiencia, Efectividad, entre otros. Sin embargo, es importante que los autores mejoren ciertos aspectos del artículo que le bajan la calidad de investigación que debe tener un trabajo. Por ejemplo:  Es recomendable mejorar la calidad del inglés. Tal como se presenta el artículo se hace difícil leerlo y entender lo que se expresa.  El título no es muy adecuado para lo que se expresa en la introducción y las conclusiones. Es recomendable considerar una modificación del título que indique claramente lo que se trabajó en el documento. El trabajo está enfocado a analizar las opiniones y trabajos realizados por diferentes autores (revisión de literatura) respecto a las técnicas de elicitación de requisitos.  El trabajo presenta un aporte importante estableciendo una clasificación a partir de categorías y dominios, al parecer identificados por los autores. Sin embargo, dichas categorías y dominios han sido identificados sin indicar qué autores proponen dicha clasificación o bien, si son los autores del artículo, de qué forma (metodología o criterios) se establecieron para definir los criterios y los dominios.
Output: es
This paper evaluates several strategies to reduce output vocabulary size in order to speed up NMT decoding and training. It could be quite useful to practitioners, although the main contributions of the paper seem somewhat orthogonal to representation learning and neural networks, and I am not sure ICLR is the ideal venue for this work.  - Do the reported decoding times take into account the vocabulary reduction step? - Aside from machine translation, might there be applications to other settings such as language modeling, where large vocabulary is also a scalability challenge? - The proposed methods are helpful because of the difficulties induced by using a word-level model. But (at least in my opinion) starting from a character or even lower-level abstraction seems to be the obvious solution to the huge vocabulary problem.
Output: en
Paper Summary:  Authors investigate identity re-parametrization in the linear and the non linear case.   Detailed comments:  — Linear Residual Network:  The paper shows that for a linear residual network any critical point is a global optimum. This problem is non convex it is interesting that this simple re-parametrization leads to such a result.    — Non linear Residual Network:  Authors propose a construction that maps the points to their labels via a resnet , using an initial random projection, followed by a residual block that clusters the data based on their label, and a last layer that maps the clusters to the label.   1- In Eq 3.4  seems the dimensions are not matching q_j in R^k and e_j in R^r. please clarify   2- The construction seems fine, but what is special about the resnet here in this construction? One can do a similar construction if we did not have the identity? can you discuss this point? In the linear case it is clear from a spectral point of view how the identity is helping the optimization. Please provide some intuition.    3-   Existence of a network in the residual  class that overfits does it give us any intuition on why residual network outperform other architectures? What does an existence result of such a network tell us about its representation power ?  A simple linear model under the assumption that points can not be too close can overfit the data, and get fast convergence rate (see for instance tsybakov noise condition).  4- What does the construction tell us about the number of layers?   5- clustering the activation independently from the label, is an old way to pretrain the network. One could use those centroids as weights for the next layer (this is also related to Nystrom approximation see for instance
Output: en
El tema es interesante y está bien estructurado en lo que se refiere a diseño de la propuesta.  El tema trata de un enfoque para la mejora, sin embargo solo establece una propuesta de arquitectura, centrándose en el diseño, y no indica cómo se evaluará o medirá esta mejora para los SDW, se recomienda agregar métricas u otros aspectos para ello. No entrega muchos detalles, se recomienda aumentar el nro. de páginas para sí mejorar este aspecto. No hay muchas referencias bibliográficas a trabajos o esfuerzos relacionados sobre el tema.
Output: es
This paper tests zoneout against a variety of datasets - character level, word level, and pMNIST classification - showing applicability in a wide range of scenarios. While zoneout acts as a regularizer to prevent overfitting, it also has similarities to residual connections. The continued analysis of this aspect, including analyzing how the gradient flow improves the given tasks, is of great interest and helps show it as an inherent property of zoneout.  This is a well written paper with a variety of experiments that support the claims. I have also previously used this technique in a recurrent setting and am confident on the positive impact it can have upon tasks. This is likely to become a standard technique used within RNNs across various frameworks.
Output: en
This is a nice paper on morphological segmentation utilizing word  embeddings. The paper presents a system which uses word embeddings to  both measure local semantic similarity of word pairs with a potential  morphological relation, and global information about the semantic validity of potential morphological segment types. The paper is well written and  represents a nice extension to earlier approaches on semantically driven  morphological segmentation.  The authors present experiments on Morpho Challenge data for three  languages: English, Turkish and Finnish. These languages exhibit varying  degrees of morphological complexity. All systems are trained on Wikipedia  text.   The authors show that the proposed MORSE system delivers clear  improvements w.r.t. F1-score for English and Turkish compared to the well  known Morfessor system which was used as baseline. The system fails to  reach the performance of Morfessor for Finnish. As the authors note, this  is probably a result of the richness of Finnish morphology which leads to  data sparsity and, therefore, reduced quality of word embeddings. To  improve the performance for Finnish and other languages with a similar  degree of morphological complexity, the authors could consider word  embeddings which take into account sub-word information. For example,  @article{DBLP:journals/corr/CaoR16,   author    = {Kris Cao and                Marek Rei},   title     = {A Joint Model for Word Embedding and Word Morphology},   journal   = {CoRR},   volume    = {abs/1606.02601},   year                  = {2016},   url                 = {[Link]},   timestamp = {Fri, 01 Jul 2016 17:39:49 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  @article{DBLP:journals/corr/BojanowskiGJM16,   author    = {Piotr Bojanowski and                Edouard Grave and                Armand Joulin and                Tomas Mikolov},   title     = {Enriching Word Vectors with Subword Information},   journal   = {CoRR},   volume    = {abs/1607.04606},   year                  = {2016},   url                 = {[Link]},   timestamp = {Tue, 02 Aug 2016 12:59:27 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  The authors critique the existing Morpho Challenge data sets.  For example, there are many instances of incorrectly segmented words in  the material. Moreover, the authors note that, while some segmentations  in the the data set may be historically valid (for example the  segmentation of business into busi-ness), these segmentations are no  longer semantically motivated. The authors provide a new data set  consisting of 2000 semantically motivated segmentation of English word  forms from the English Wikipedia. They show that MORSE deliver highly  substantial improvements compared to Morfessor on this data set.  In conclusion, I think this is a well written paper which presents  competitive results on the interesting task of semantically driven  morphological segmentation. The authors accompany the submission with  code and a new data set which definitely add to the value of the  submission.
Output: en
The paper presents two approaches for generating English poetry. The first approach combine a neural phonetic encoder predicting the next phoneme with a phonetic-orthographic HMM decoder computing the most likely word corresponding to a sequence of phonemes. The second approach combines a character language model with a weigthed FST to impose rythm constraints on the output of the language model. For the second approach, the authors also present a heuristic approach which permit constraining the generated poem according to theme (e.g;, love) or poetic devices (e.g., alliteration). The generated poems are evaluated both instrinsically by comparing the rythm of the generated lines with a gold standard and extrinsically by asking 70 human evaluators to (i) determine whether the poem was written by a human or a machine and (ii) rate poems wrt to readability, form and evocation.  The results indicate that the second model performs best and that human evaluators find it difficult to distinguish between human written and machine generated poems.  This is an interesting, clearly written article with novel ideas (two different models for poetry generation, one based on a phonetic language model the other on a character LM) and convincing results.   For the evaluation, more precision about the evaluators and the protocol would be good. Did all evaluators evaluate all poems and if not how many judgments were collected for each poem for each task ? You mention 9 non English native speakers. Poems are notoriously hard to read. How fluent were these ?   In the second model (character based), perhaps I missed it, but do you have a mechanism to avoid generating non words ? If not, how frequent are non words in the generated poems ?  In the first model, why use an HMM to transliterate from phonetic to an orhographic representation rather than a CRF?   Since overall, you rule out the first model as a good generic model for generating poetry, it might have been more interesting to spend less space on that model and more on the evaluation of the second model. In particular, I would have been interested in a more detailed discussion of the impact of the heuristic you use to constrain theme or poetic devices. How do these impact evaluation results ? Could they be combined to jointly constrain theme and poetic devices ?   The combination of a neural mode with a WFST is reminiscent of the following paper which combine character based neural model to generate from dialog acts with an WFST to avoid generating non words. YOu should relate your work to theirs and cite them.   Natural Language Generation through Character-Based RNNs with Finite-State Prior Knowledge Goyal, Raghav and Dymetman, Marc and Gaussier, Eric and LIG, Uni COLING 2016
Output: en
Overall, this is a nice paper. Developing a unifying framework for these newer neural models is a worthwhile endeavor.  However, it's unclear if the DRAGNN framework (in its current form) is a significant standalone contribution. The main idea is straightforward: use a transition system to unroll a computation graph. When you implement models in this way you can reuse code because modules can be mixed and matched. This is nice, but (in my opinion) is just good software engineering, not machine  learning research.  Moreover, there appears to be little incentive to use DRAGNN, as there are no 'free things' (benefits) that you get by using the framework. For example:  - If you write your neuralnet in an automatic differentiation library (e.g.,   tensorflow or dynet) you get gradients for 'free'.  - In the VW framework, there are efficiency tricks that 'the credit assignment   compiler' provides for you, which would be tedious to implement on your   own. There is also a variety of algorithms for training the model in a   principled way (i.e., without exposure bias).  I don't feel that my question about the limitations of the framework has been satisfactorily addressed. Let me ask it in a different way: Can you give me examples of a few models that I can't (nicely) express in the DRAGNN framework? What if I wanted to implement
Output: en
- Buena revisión de tendencias, tecnologías y estándares que podrían utilizarse para la implementación de sistemas de telemedicina. - Interesante caso práctico que hacen del sistema SAT y sus posibles aplicaciones desde la Ing. de Software  - Las iniciativas y tendencias que muestran en telemedicina, deberían describirlas en función de los apartados que tratan en el artículo: integración, interoperabilidad y desarrollo de aplicaciones, y no en general como lo presentan. Por esta desarticulación, es posible que no sea muy clara la consecuencia entre lo que presentan en el resumen y lo que se encuentra en el desarrollo del paper. - Considero que el título no debería enfocarse solo hacia 'Herramientas', pues al comenzar a leer el trabajo queda la idea q el aporte y la revisión es mas a nivel operativo y de sistemas que apoyen el proceso, mas no de iniciativas en cuanto a estándares, proyectos y tecnologías desarrolladas para tal fin. El título no tiene el alcande del contenido.  El artículo describe en el caso chileno, cuáles podrían ser iniciativas, tendencias y tecnologías para la aplicación de ingeniería de software en el ámbito de la telemedicina. No representa originalidad como tal en ninguna de las dos áreas.  La calidad técnica es buena en cuanto a que la revisión que describen está soportada bibliográficamente, con tendencias, tecnologías y estándares actuales. En cuanto a la escritura técnica, sí hay errores en el adecuado uso de las siglas, en algunos casos no muestran su significado, en algunos otros casos no son bien utilizadas. (Por ejemplo: Se debe decir TIC, no TICs; en la citación de las referencias en el texto, se debe suprimir la puntuación antes del número --> especialista [14].; las figuras pueden mejorarse significativamente). La descripción de las 'tendencias' e 'iniciativas' daría mayor valor al artículo que ejemplos como el incluido en esta última sección donde muestran la representación de un mensaje en sintaxis diferentes.  Es relevante para el contexto chileno, en cuando presentan una propuesta interesante para que los diferentes actores (gobierno, desarrolladores, compañías) tengan en cuenta para impulsar esta área de trabajo.  La organización del documento se puede mejorar en los aspectos nombrados en 'calidad técnica, al igual que aspectos que evidencien una consecuencia y coherencia entre título, resumen y desarrollo del artículo. Así mismo, hay errores ortográficos y de redacción que, si se evitan, pueden darle mayor valor a la presentación del artículo.
Output: es
El documento propone un modelo de visualización gráfico para arboles de decisión basado en el esquema VAM-DM que fue probado por medio de una aplicación con usuarios novatos y expertos.  El documento presenta varios problemas de forma más que de fondo.  Tiene errores de ortografía, en la puntuación y tipográficos.  En algunas partes parece ser una traducción mal hecha. Todo esto dificulta la comprensión del texto.  El significado de las siglas debe explicarse desde su primera utilización.  Faltan referencias en el lugar apropiado. Por ejemplo: desde la primera vez que se habla del esquema VAM-DM se debe poner la referencia o se utiliza la palabra varios autores sin referencias.  La encuesta debería estar disponible, ya sea en un Anexo o en algún sitio en Internet.
Output: es
El artículo presenta un estudio de la percepción que tienen los ingenieros de requisitos (noveles y expertos) en relación a la efectividad de las diferentes técnicas de educción  para el descubrimiento de los requisitos en un proyecto de desarrollo de software.  El articulo trata un tema que merece cierta atención ante la gran cantidad de técnicas de educción que existen y la desorientación de los ingenieros de requisitos respecto de cuál utilizar en un determinado contexto.  El estudio realizado considera como noveles Ingenieros que no tienen conocimiento de técnicas de educción y uno de los atributos que debe ser calificado como base del estudio comparativo, es precisamente su experiencia. De hecho como conclusión se expresa que “una más amplia formación y principalmente de tipo práctica, es necesaria para que los ingenieros noveles puedan decidir….”, eso es de Perogrullo. Debería ser considera una gama mayor de atributos del contexto como los sugeridos en [14], que permitan conclusiones más veraces.
Output: es
Un Objeto de Aprendizaje sirve como apoyo al proceso educativo, para desarrollar competencias en los estudiantes en alguno de los tres tipos de saberes (domino conceptual, dominio de procedimientos y desarrollo de actitudes y valores). En ninguno de estos ámbitos se dan evidencias que lo demuestren. Además de que las experiencias son muy básicas, el abstract, lo cito textual: "El Artículo presenta, las experiencias recabadas de la aplicación de Objetos de Aprendizaje en el desarrollo de la asignatura de Introducción a la Teoría de Autómatas, debido a que en el desarrollo del curso se han desplegado cambios significativos, planteando un seguimiento académico detallado y exhaustivo mediante un esquema de “Retroalimentación Rápida” y “Preguntas Cortas”.", no se muestran las experiencias recabadas ni tampoco se menciona como las obtendrían.
Output: es
- Strengths: This paper presents an extension to A* CCG parsing to include dependency information.  Achieving this while maintaining speed and tractability is a very impressive feature of this approach.  The ability to precompute attachments is a nice trick.                  I also really appreciated the evaluation of the effect of the head-rules on normal-form violations and would love to see more details on the remaining cases.  - Weaknesses: I'd like to see more analysis of certain dependency structures.  I'm particularly interested in how coordination and relative clauses are handled when the predicate argument structure of CCG is at odds with the dependency structures normally used by other dependency parsers.  - General Discussion: I'm very happy with this work and feel it's a very nice contribution to the literature.  The only thing missing for me is a more in-depth analysis of the types of constructions which saw the most improvement (English and Japanese) and a discussion (mentioned above) reconciling Pred-Arg dependencies with those of other parsers.
Output: en
Studying the Hessian in deep learning, the experiments in this paper suggest that the eigenvalue distribution is concentrated around zero and the non zero eigenvalues are related to the complexity of the input data. I find most of the discussions and experiments to be interesting and insightful. However, the current paper could be significantly improved.  Quality: It seems that the arguments in the paper could be enhanced by more effort and more comprehensive experiments. Performing some of the experiments discussed in the conclusion could certainly help a lot. Some other suggestions: 1- It would be very helpful to add other plots showing the distribution of eigenvalues for some other machine learning method for the purpose of comparison to deep learning. 2- There are some issues about the scaling of the weights and it make sense to normalize the weights each time before calculating the Hessian otherwise the result might be misleading. 3- It might worth trying to find a quantity that measures the singularity of Hessian because it is difficult to visually conclude something from the plots. 4- Adding some plots for the Hessian during the optimization is definitely needed because we mostly care about the Hessian during the optimization not after the convergence.  Clarity: 1- There is no reference to figures in the main text which makes it confusing for the reading to know the context for each figure. For example, when looking at Figure 1, it is not clear that the Hessian is calculated at the beginning of optimization or after convergence. 2- The texts in the figures are very small and hard to read.
Output: en
El artículo presenta una aplicación para celulares que ayudaría al ciudadano mexicano a mejorar la gestión del pago de los impuestos federales. El principal problema de este artículo es que no muestra  el uso de técnicas de construcción de softwares ni esgrime detalles de la propia solución (la arquitectura del software).  Un segundo punto más fuerte para mi evaluación negativa, es la pésima calidad del texto. Por ejemplo:  1) Errores tipográficos, como por ejemplo "fiscas" en el resumen  2) Frases largas y confusas como en el primer parágrafo de la introducción y las dos últimas de la columna  izquierda en la página 2.  3) Frases que no respetan la concordancia de género y número como por ejemplo "la investigaciones está organizado" en la página 2.  4) El cuadro 2 es citado antes del cuadro 1, no existe un cuadro 3, en el cuadro 4 los porcentajes están errados, y  el cuadro 5 además de no ser citado en el texto, contiene intervalos muy pequeños (podría juntar los dos intervalos de edad intermediarios) y trabaja con edades fraccionadas...es mejor tener porcentajes menos redondos que edades fraccionadas.  5) El texto solo tiene dos secciones: Introducción y Conclusión (que ellos llamaron de reflexiones).  6) Las referencias no siguen un padrón único.
Output: es
El artículo propone la aplicación de Diagramas de Clases UML Orientados a Aspectos JPI. En general, el artículo está bien planteado y no hay grandes observaciones. Es más, se observa una preocupación por hacer una buena presentación del trabajo. Sólo haría algunas sugerencias menores, especialmente, con los aspectos metodológicos que deberían ser un poco más detallados.
Output: es