niryuu/sni-each-converted huggingface dataset:
This paper proposes a novel strategy for zero-resource translation where (source, pivot) and (pivot, target) parallel corpora are available. A teacher model for p(target|pivot) is first trained on the (pivot, target) corpus, then a student model for p(target|source) is trained to minimize relative entropy with respect to the teacher on the (source, pivot) corpus. When using word-level relative entropy over samples from the teacher, this approach is shown to outperform previous variants on standard pivoting, as well as other zero-resource strategies.  This is a good contribution: a novel idea, clearly explained, and with convincing empirical support. Unlike some previous work, it makes fairly minimal assumptions about the nature of the NMT systems involved, and hence should be widely applicable.  I have only a few suggestions for further experiments. First, it would be interesting to see how robust this approach is to more dissimilar source and pivot languages, where intuitively the true p(target|source) and p(target|pivot) will be further apart. Second, given the success of introducing word-based diversity, it was surprising not to see a sentence n-best or sentence-sampling experiment. This would be more costly, but not much more so since you’re already doing beam search with the teacher. Finally, related to the previous, it might be interesting to explore transition from word-based diversity to sentence-based as the student converges and no longer needs the signal from low-probability words.  Some further comments:  line 241: Despite its simplicity -> Due to its simplicity  277: target sentence y -> target word y  442: I assume that K=1 and K=5 mean that you compare probabilities of the most probable and 5 most probable words in the current context. If so, how is the current context determined - greedily or with a beam?  Section 4.2. The comparison with an essentially uniform distribution doesn’t seem very informative here: it would be extremely surprising if p(y|z) were not significantly closer to p(y|x) than to uniform. It would be more interesting to know to what extent p(y|z) still provides a useful signal as p(y|x) gets better. This would be easy to measure by comparing p(y|z) to models for p(y|x) trained on different amounts of data or for different numbers of iterations. Another useful thing to explore in this section would be the effect of the mode approximation compared to n-best for sentence-level scores.  555: It’s odd that word beam does worse than word greedy, since word beam should be closer to word sampling. Do you have an explanation for this?  582: The claimed advantage of sent-beam here looks like it may just be noise, given the high variance of these curves.
Output: en
Interesante trabajo.  si bien el documento muestra una propuesta, sería interesante mostrar aplicaciones.
Output: es
- Strengths: The paper proposes an end-to-end neural model for semantic graph parsing, based on a well-designed transition system.  The work is interesting, learning semantic representations of DMRS, which is capable of resolving semantics such as scope underspecification. This work shows a new scheme for computational semantics, benefiting from an end-to-end transition-based incremental framework, which resolves the parsing with low cost.  - Weaknesses:   My major concern is that the paper only gives a very common introduction for the definition of DMRS and EP, and the example even makes me a little confused because I cannot see anything special for DMRS. The description can be a little more detailed, I think. However, upon the space limitation, it is understandable. The same problem exists for the transition system of the parsing model. If I do not have any background of MRS and EP, I can hardly learn something from the paper, just seeing that this paper is very good.  - General Discussion:   Overall, this paper is very interesting to me. I like the DMRS for semantic parsing very much and like the paper very much. Hope that the open-source codes and datasets can make this line of research being a hot topic.
Output: en
El paper describe el contexto actual del uso de tecnologías disponibles para controlar de manera automática la dosificación de macronutrientes en los tratados de cultivos sin suelo.  El trabajo es pertinente en su definición y desarrollo. Sin embargo, el título induce a una expectativa que no se logra satisfacer en el documento. El paper ofrece una discusión sobre el estado del arte en tecnología para la problemática abordada, sin embargo, no ofrece un framework comparativo, criterios de selección, o sugerencia sobre buenas prácticas.  Como primer acercamiento al área de investigación es pertinente, sin embargo, algo más de trabajo propio debe ser añadido al paper.
Output: es
I reviewed the manuscript as of December 6th.  The authors perform a systematic investigation of various retraining methods for making a classification network robust to adversarial examples. The authors achieve lower error rates using their RAD and IAEC methods perform better then previously introduced distillation methods for retraining networks to be robust to adversarial examples. This method suggests a promising direction for building a defense for adversarial examples.  Major Comments: I find the paper to not be lacking in exposition and clarity. The paper has a laundry list of related results (page 2) but no clear message. I *think* one larger point is the superior performance of their retraining techniques but it is not clear how well these techniques perform compared to other retraining techniques, nor are the details of the retraining techniques clear. The paper requires more discussion and a clear exposition about the methods the authors introduced (i.e. RAD, IAEC). What follow are some more detailed comments along this theme of improving the exposition and clarity:  - The authors should provide more details about how they constructed the auto-encoder in the IAEC method (diagram?). The same needs to be said for the RAD method. The authors point to a previous workshop submission (
Output: en
Fondo: - Se menciona de manera muy superficial el lenguaje a utilizar en forma posterior a Scratch, quizás no es necesario o se debe ahondar más en el tema. - El número de alumnos sobre el cual se aplicó el estudio es muy pequeño para obtener resultados concluyentes. - Sería deseable una comparación o estudio entre la utilización de Scratch y Alice (mencionado en el trabajo). - Concluir a partir del experimento que Scratch es una herramienta adecuada para la educación en Chile, sin incluir resultados con otros lenguajes es un poco apresurado. - Sería deseable mencionar que instituciones o trabajos relacionados con lenguajes para fomentar el pensamiento lógico se han realizado en Chile. No se menciona ninguno como referencia o en la bibliografía. - El ejercicio utilizado en el experimento es uno sólo, se deberían utilizar más si se desea objetivamente evaluar su utilización como lenguaje para mejorar el razonamiento lógico. - La primera cita proviene de un portal de noticias Colombiano y del 2006. Sería deseable una referencia más sólida y local asociada quizás a los lineamientos y planes de estudio del ministerio de educación.   Forma: - Se debe re-escribir el abstract: posee muchas palabras separadas por comas y la redacción incluye ideas desconectadas entre sí. - Se cita 8 veces la cita 7/8. Con la mitad de ellas queda clara la referencia. - ".. pero pocos aprender a programar .." debería ser: ".. pero pocos aprendeN a programar ..". - La cita 2 dice .. "Fundamentos de POgramacion".. debería ser: ".. Fundamentos de Programación".
Output: es
El artículo presenta en detalle el proceso realizado para la digitalización del archivo histórico de la parroquia San Pedro de Coquimbo. Se entregan los antecedentes previos, el análisis realizado y se explica la solución implementada.  Los autores muestran claramente los pasos realizados para implementar la solución propuesta.  Si bien la solución propuesta es correcta para el problema planteado, no se destaca un aporte significativo respecto a previas soluciones tecnológicas asociadas a digitalización de documentos.  El artículo no muestra resultados o evaluaciones realizadas al sistema implementado.
Output: es
This paper addresses an important and timely topic in a creative way. I consider it to have three flaws (and one good idea).  1) insufficient context of what is known and had been studied before (in shallow RL), for example within the field of “robust RL”. A good place to start might be with the work of Shie Mannor.  2) an ill-defined general problem setup. Does it make sense to do post-hoc labeling of certain actions as “catastrophic” if the agent is not informed about that metric during learning? Training a system to do one thing (maximize reward), but then evaluating it with a different metric is misleading. On the training metric, it could even be that the baseline outperforms the new algorithm? So I’d want to see plots for “average reward” in fig 3 as well. Also, what would the baseline learn if it was given large negative rewards for entering these otherwise invisible “danger states”?  3) a somewhat ad-hoc solution, that introduces new domain-specific hyperparameters (k_r, k_lambda and lambda) a second deep network and and two additional replay memories. In terms of results, I’m also unsure whether I can trust the results, given the long-standing track-record of cart-pole being fully solved by many methods: is DQN an outlier here? Or is the convnet not an appropriate function-approximator? Actually: which exact variant “state-of-the-art” variant of DQN are you using?  The good idea that I encourage the authors to pursue further is D_d, this set of rare but dangerous states, that should be kept around in some form. I see it as an ingredient for continual learning that most typical methods lack -- it is also one of the big differences between RL and supervised learning, where such states would generally be discarded as outliers.  Given my comment a couple of weeks ago, and the prompt response (“we implemented expected SARSA”), I would have expected that the paper had been revised with the new results by now? In any case, I’m open to discussing all these points and revising my opinion based on an updated version of the paper.  Minor comment: the bibliography is done sloppily, with missing years, conference venues and missing/misspelled author lists, e.g. “Sergey et al. Levine”. I also think it is good form to cite the actual conference publications instead of arXiv where applicable.
Output: en
Two things I really liked about this paper: 1. The whole idea of having a data-dependent proposal distribution for MCMC. I wasn't familiar with this, although it apparently was previously published. I went back: the (Zhu, 2000) paper was unreadable. The (Jampani, 2014) paper on informed sampling was good. So, perhaps this isn't a good reason for accepting to ICLR.  2. The results are quite impressive. The rough rule-of-thumb is that optimization can help you speed up code by 10%. The standard MCMC results presented on the paper on randomly-generated programs roughly matches this (15%). The fact that the proposed algorithm get ~33% speedup is quite surprising, and worth publishing.  The argument against accepting this paper is that it doesn't match the goals of ICLR. I don't go to ICLR to hear about generic machine learning papers (we have NIPS and ICML for that). Instead, I go to learn about how to automatically represent data and models. Now, maybe this paper talks about how to represent (generated) programs, so it tangentially lives under the umbrella of ICLR. But it will compete against more relevant papers in the conference -- it may just be a poster. Sending this to a programming language conference may have more eventual impact.  Nonetheless, I give this paper an "accept", because I learned something valuable and the results are very good.
Output: en
NO ME ENCUENTRO CAPACITADO PARA EFECTUAR ESTA REVISION. ESTA MUY LEJOS DE MI ESPECIALIZACION.
Output: es
Presenta un trabajo de gran relevancia para la ciencia en Chile, mencionando elementos muy necesarios para el desarrollo de las comunicaciones de alto desempeño en los polos tecnológicos y de investigación  Podría profundizar en una breve descripción técnica de la tecnología que se menciona en el paper, dado que el trabajo no es autocontenido.
Output: es
El artículo describe una interesante aplicación de control de un dispositivo físico (para el caso en estudio, un robot móvil de Lego Mindstorms), mediante el procesamiento de las señales derivadas de cambios de índole facial y/o mental, provenientes de un casco  Emotiv EEG, el cual realiza un electroencefalograma de alta resolución. Sin embargo a pesar de lo interesante del trabajo presentado, el artículo carece del rigor que caracteriza al proceso metodológico de investigación, no se observa una discusión bibliográfica critica, no hay una definición clara del problema a investigar, presenta una pobre discusión  de resultados y abundantes errores de forma (Ej. la página 3  presenta el siguiente texto “¡Error! No se encuentra el origen de la referencia”, no existe página 2, etc.), que denotan que el autor no fue riguroso en su revisión antes del envío. Finalmente en mi opinión el trabajo es un buen aporte como un excelente trabajo de proyecto o taller dentro la malla curricular de alguna carrera de Informática o Electrónica.
Output: es
El paper corresponde a una muy buena aplicación de una plataforma para interfaz gestual de acciones con un robot. Esta bien redactado y con buen uso de las referencias
Output: es
The authors apply the image captioning architecture of Xu et al. 2015 to video captioning. The model is extended to have attention over multiple layers of the ConvNet instead of just a single layer. Experiments on YouTube2Text, M-VAD and MSR-VTT show that this works better than only using one of the layers at a time.  I think this is solid work on the level of a well-executed course project or a workshop paper. The model makes sense, it is adequately described, and the experiments show that attending over multiple layers works better than attending over any one layer in isolation. Unfortunately, I don't think there is enough to get excited about here from a technical perspective and it's not clear what value the paper brings to the community. Other aspects of the paper, such as including the hard attention component, don't seem to add to the paper but take up space.   If the authors want to contribute a detailed, focused exploration of multi-level features this could become a more valuable paper, but in that case I would expect a much more thorough exploration of the choices and tradeoffs of different schemes without too many spurious aspects such as video features, hard attention, etc.
Output: en
This paper is very interesting, I like the idea, and to make it a work publishable in vennue reaching a wider international audience it is necessary to validate the proposed ideas through extensive experimentation with actual data and users, please go ahead.
Output: en
## Paper summary  The paper reconsiders the idea of using a binary classifier to do two-sample testing. The idea is to split the sample into two disjoint training and test sets, train a classifier on the training set, and use the accuracy on the test set as the test statistic. If the accuracy is above chance level, one concludes that the two samples are from different distributions i.e., reject H0.  A theoretical result on an asymptotic approximate test power is provided. One implication is that the test is consistent, assuming that the classifier is better than coin tossing. Experiments on toy problems, evaluation of GANs, and causal discovery verify the effectiveness of the test. In addition, when the classifier is a neural net, examining the first linear filter layer allows one to see features which are most activated. The result is an interpretable visual indicator of how the two samples differ.  ## Review summary   The paper is well written and easy to follow. The idea of using a binary classifier for a two-sample testing is not new, as made clear in the paper. The main contributions are the analysis of the asymptotic test power, the use of modern deep nets as the classifier in this context, and the empirical studies on various tasks. The empirical results are satisfactorily convincing.  Although not much discussion is made on why the method works well in practice, overall contributions have a potential to start a new direction of research on model criticisms of generative models, as well as visualization of where a model fails. I vote for an acceptance.  ## Major comments / questions   My main concern is on Theorem 1 (asymptotic test power) and its assumptions.  But, I understand that these can be fixed as discussed below.  * Under H0, the distribution of the test statistic (i.e., sum of 0-1 classification results) follows Binomial(nte, 1/2) as stated.  However, under H1, terms in the sum are independent but *not* identical Bernoulli random variable. This is because each term depends on a data point z_i, which can be from either P or Q. So, in the paragraph in Sec3.1: "... the random variable n_te \hat{t} follows a Binomial(nte, p)..." is not correct. Essentially p depends on z_i. It should follow a Poisson binomial distribution.  * In the same paragraph, for the same reason, the alternative distribution of Binomial(nte, p=p_{risk}) is probably not correct. I guess you mention it to use Moivre-Laplace to get the asymptotic normality.   Anyway, I see no reason why you would need this statement as the Binomial is not required in the proof, but only its asymptotic normality. A variant of the central limit theorem (instead of the Moivre-Laplace theorem) for independent, non-identical variables would still allow you to conclude the asymptotic normality of the Poisson binomial (with some conditions). See for example
Output: en
Important task (attention models), interesting distillation application, well-written paper. The authors have been responsive in updating the paper, adding new experiments, and being balanced in presenting their findings. I support accepting this paper.
Output: en
This paper proposes Generative Adversarial Parallelization (GAP), one schedule to train N Generative Adversarial Networks (GANs) in parallel. GAP proceeds by shuffling the assignments between the N generators and the N discriminators at play every few epochs. Therefore, GAP forces each generator to compete with multiple discriminators at random. The authors claim that such randomization reduces undesired "mode collapsing behaviour", typical of GANs.  I have three concerns with this submission.  1) After training the N GANs for a sufficient amount of time, the authors propose to choose the best generator using the GAM metric. I oppose to this because of two reasons. First, a single GAN will most likely be unable to express the full richness of the true data begin modeled. Said differently, a single generator with limited power will either describe a mode well, or describe many modes poorly. Second, GAM relies on the scores given by the discriminators, which can be ill-posed (focus on artifacts). Since there is There is nothing wrong with mode collapsing when this happens under control. Thus, I believe that a better strategy would be to not choose and combine all generators into a mixture. Of course, this would require a way to decide on mixture weights. This can be done, for instance, using rejection sampling based on discriminator scores.  2) The authors should provide a theoretical (or at least conceptual) comparison to dropout. In essence, this paper has a very similar flavour: every generator is competing against all N discriminators, but at each epoch we drop N-1 for every generator. Related to the previous point, after training dropout keeps all the neurons, effectively approximating a large ensemble of neural networks.  3) The qualitative results are not convincing. Most of the figures show only results about GAP. How do the baseline samples look like? The GAN and LAPGAN papers show very similar samples. On the other hand, I do not find Figures 3 and 4 convincing: for instance, the generator in Figure 3 was most likely under-parametrized.  As a minor comment, I would remove Figure 2. This is because of three reasons: it may be protected by copyright, it occupies a lot of space, and it does not add much value to the explanation. Also, the indices (i_t) are undefined in Algorithm 1.  Overall, this paper shows good ideas, but it needs further work in terms of conceptual development and experimental evaluation.
Output: en
Este artículo propone un sistema que agrega componentes difusos a ciertos atributos de una base de datos con información sobre el estado físico de personas.  La aplicación del trabajo es interesante, ya que son escasas las experiencias donde en la práctica se utilicen atributos difusos para almacenamiento y consulta en nuestro país, por lo que puede hacer que el tema tratado sea interesante para nuestro congreso.  Sin embargo los siguientes aspectos, o no quedan claros o están incompletos: - Utilizar formato solicitado por el congreso. - Mejorar redacción general del resumen/introducción - Redactar en tercera persona el punto 3, 4.1.1 cuando se refiere al cliente - Cuidar el uso de abreviaturas que a veces se usan en minúsculas o mayúsculas - Redactar la conclusión con un enfoque donde se deje claro qué beneficios que obtuvo el usuario del sistema con respecto a un sistema no difuso - No queda claro cómo se determinaron las funciones de pertenencia utilizadas. - Falto algún tipo de estudio/comparativa para comprobar el aporte que un sistema como el propuesto mejora o complementa un sistema tradicional en el área aplicada  Debido a lo anterior, se recomienda aceptar el artículo para su presentación en el Workshop de aplicaciones empresariales.
Output: es
This paper presents a variational inference based method for learning nonlinear dynamical systems. Unlike the deep Kalman filter, the proposed method learns a state space model, which forces the latent state to maintain all of the information relevant to predictions, rather than leaving it implicit in the observations. Experiments show the proposed method is better able to learn meaningful representations of sequence data.  The proposed DVBF is well motivated, and for the most part the presentation is clear. The experiments show interesting results on illustrative toy examples. I think the contribution is interesting and potentially useful, so I’d recommend acceptance.  The SVAE method of Johnson et al. (2016) deserves more discussion than the two sentences devoted to it, since the method seems pretty closely related. Like the DVBF, the SVAE imposes a Markovianity assumption, and it is able to handle similar kinds of problems. From what I understand, the most important algorithmic difference is that the SVAE q network predicts potentials, whereas the DVBF q network predicts innovations. What are the tradeoffs between the two?  Section 2.2 says they do the latter in the interest of solving control-related tasks, but I’m not clear why this follows.   Is there a reason SVAEs don’t meet all the desiderata mentioned at the end of the Introduction?  Since the SVAE code is publicly available, one could probably compare against it in the experiments.   I’m a bit confused about the role of uncertainty about v. In principle, one could estimate the transition parameters by maximum likelihood (i.e. fitting a point estimate of v), but this isn’t what’s done. Instead, v is integrated out as part of the marginal likelihood, which I interpret as giving the flexibility to model different dynamics for different sequences. But if this is the case, then shouldn’t the q distribution for v depend on the data, rather than being data-independent as in Eqn. (9)?
Output: en
En esta investigación se trata un tema que cada vez es más importante en ámbitos académicos y empresariales. Por otra parte, se utilizan métodos de análisis de datos complejos y muy adecuados a los objetivos de la investigación y los autores los que sirven de referencia básica para la investigación son muy adecuados.  En la medida en que el objeto material de la investigación es el individuo, entiendo que, cuando se describe la muestra, es conveniente, no sólo describir el perfil de la empresa, sino el perfil del individuo entrevistado (edad, sexo, puesto que ocupa, etc.). •	El apartado conclusiones merece, a mi juicio, una mayor atención, que incluya no sólo los resultados de la investigación, sino una discusión más amplia. •	Las limitaciones de la investigación hacen referencia al tamaño de la muestra y al tipo de muestreo empleado, pero no hacen referencia a la dimensión del modelo. En este sentido, sería conveniente plantear, como línea de investigación futura, la ampliación del modelo con nuevas variables e indicadores •	las referencias bibliográficas son anteriores al año 2009. Sugiero consultar las siguientes o	The effect of organizational support on ERP implementation DonHee Lee,  Sang M. Lee, David L. Olson, Soong Hwan Chung.  Industrial Management + Data Systems.  Wembley:2010.  Vol. 110,  Iss. 2,  p. 269-283 o	Predicting the behavioral intention to use enterprise resource planning systems :An exploratory extension of the technology acceptance model Fethi Calisir,  Cigdem Altin Gumussoy,  Armagan Bayram.  Management Research News.  Patrington:2009.  Vol. 32,  Iss. 7,  p. 597-613 o	Organizational adoption of information technologies: Case of enterprise resource planning systems Onur Kerimoglu,  Nuri Basoglu,  Tugrul Daim.  Journal of High Technology Management Research.  Greenwich:2008.  Vol. 19,  Iss. 1,  p. 21
Output: es
En este trabajo se presenta  una revisión de Sistemas Inmunes Artificiales  y  sus aplicaciones.  Observaciones:  Un trabajo de revisión tiene que ser cuidadoso y amplio, con comentarios críticos, sugerencias y conclusiones que sirvan como contribuciones del trabajo.  El artículo propuesto no incluye en la revisión trabajos  y aplicaciones recientes.  Luego, la revisión no es completa.  Por otro lado, no presenta  discusión crítica ni conclusiones de la revisión realizada. En consecuencia, el  autor no  alcanzó  ninguno de los dos objetivos esperados  para este trabajo.
Output: es
Reviewers agree that the findings are not clear enough to be of interest, though the effort to do a controlled study is appreciated.
Output: en
•I recommend to change the paper title in order to be according to paper‘ body •Review the English grammar, some but not all of the errors founded in the review are:  •Abstract   o what does it mean that for them?--> what does it mean that for them?   o …utilized to represent its appropriateness.--> …utilized to represent their appropriateness.   •Introducction   o …has like first phase… --> ?   o auscultating --> is it a verb?   •Background   o A systematic mapping study is a methodology that is used --> A systematic mapping study is a methodology used   o …of existing research on performance evaluation… -->…of existing research on the performance evaluation…   o Only Dieste and Juristo’s work [10] deals --> Only the work of Dieste and Juristo [10] deals   •Methodology   o Review the use of –ly with the verb to be   o Third question: What kind of studies does use with a higher frequency certain constructs for   o appropriateness of techniques? --> ? review the types question structure   •Classification schema   o In order to answer the research questions, the literature review focused --> literature or mapping?   •Analysis of the result   o in Figure 1 en DCM format -->in Figure 1 en DCM format.   o article --> paper   •Conclusions and future works   o For this reason, it is necessary that more proposals are carried out to help --> For this reason, it is necessary to carry out more proposals to help   •Figures: be careful on the figure´s title, the title must be following the figure  •Type of studies  •Paragraph after figure 5: Experimental (paragraph) o experiment (figure)  •Techniques considered: I recommend performing a mapping between domains and techniques and or with the constructors and show the a graphic of the results instead of the figure 8
Output: en
- Strengths:  When introducing the task, the authors use illustrative examples as well as the contributions of this paper.  Related Works section covers the state of the art, at the same time pointing similarities and differences between related Works and the proposed method. The presentation of the method is very clear, since the authors separate the tagging scheme and the end-to-end model. Another strong point of this work is the baselines used to compare the proposed methods with several classical triplet extraction methods. At last, the presentation of examples from dataset used to illustrate the advantages and disadvantages of the methods was very important. These outputs complement the explanation of tagging and evaluation of triplets.   - Weaknesses:  One of the main contributions of this paper is a new tagging scheme described in Section 3.1, however there are already other schemes for NER and RE being used, such as IO, BIO and BILOU.  Did the authors perform any experiment using other tagging scheme for this method? Regarding the dataset, in line 14, page 5, the authors cite the number of relations (24), but they do not mention the number or the type of named entities. In Section 4.1, the evaluation criteria of triplets are presented. These criteria were based on previous work? As I see it, the stage of entity identification is not complete if you consider only the head of the entity. Regarding example S3, shown in Table 3, the output of the LSTM-LSTM-Bias was considered correct? The text states that the relation role is wrong, although it is not clear if the relation role is considered in the evaluation.   - General Discussion:  This paper proposes a novel tagging scheme and investigates the end-to-end models to jointly extract entities and relations.  The article is organized in a clear way and it is well written, which makes it easy to understand the proposed method.
Output: en
El artículo está bien estructurado, respeta el formato establecido. Se presentan dos propuestas asociadas a distintas etapas de los procesos de cocreación de valor. Ambas propuestas se presentan de forma resumida. Se sugiere extender un poco más la presentación de cada una de ella, y ejemplificarlas mediante su aplicación en algún caso de estudio. Se observan algunos errores de acentuación y de edición en algunas partes (Ej. Sección Resultados, "se validó la el modelo...") Se observa un problema de formato en las referencias. Algunas de las referencias bibliográficas son muy antiguas.
Output: es
El artículo describe básicamente los componentes de un aerodeslizador, y el sensor que podría ser utilizado para el control de navegación.  Es un estudio más bien teórico que no presenta evidencias de su construcción.  No queda claro si la propuesta ya fue publicada por el inserto en pie de página.  Se deben corregir varios errores de tipo gramatical; Ejemplos: Primer párrafo introducción ”con el término” y no “el término” C. Sistema de propulsión … a los cuales se acoplará Texto figura 2 debe incorporarse al pie de la figura Figura 3 sin título Figura 6 después de la 3? Penúltimo párrafo hoja 4 (“con estés….”) Enumerar ecuaciones.
Output: es
La verificación de la arquitectura de un producto software puede ser una gran contribución a la calidad del mismo, por lo cual, cualquier herramienta que facilite esta labor es un aporte a la ingeniería de software. Presentan evidencias de una revisión previa de métodos y herramientas alternativas o complementarias a la propuesta. Las descripciones presentadas se acompañan de ejemplos aclaratorios.  No queda claro el rol e interacción del experto del dominio. En las secciones iniciales dice que la herramienta realiza análisis estático y dinámico, sin embargo al final del artículo, en la identificación de posibles mejoras de SAVE se propone incluir el análisis dinámico, lo que se contradice con la afirmación inicial. La descripción del funcionamiento de la herramienta no está lo suficientemente claro. La calidad de la figuras no es adecuada, por el tamaño y resolución de las mismas no es posible apreciar bien los detalles. La redacción de artículo puede ser mejorada. Hay varias frases en donde faltan conectores, por ejemplo "En el presente apartado, veremos S.A.V.E. logra recuperar las dependencias existentes en el código fuente...." Muchos errores en la acentuación de las palabras.
Output: es
This work investigates the performance of transfer learning from resource-rich setup (BookTest, CNN/Daily Mail corpora) to low-resource (bAbI, SQuAD benchmarks) settings. Experiments show poor improvements in 0-shot learning. However, when the model is exposed to few training instances some improvements are observed.  The claims made here require a more comprehensive analysis. I criticize the use of bAbI as a low-resource real-world scenario. bAbI is designed as a unit test and is far from representing many natural language phenomena. Thus, the claims related to bAbI can only be weak evidence for questioning transfer learning high-resource to low-resource in real-world scenarios. I highly recommend using recently proposed real-world scenarios [1,2].  More importantly, the work does not explain why and how do we get improvement using transfer learning. They remotely address this by hypothesizing the knowledge of transfer is not just encoded in embeddings but also in the model. Considering the related work [3], these claims bring a marginal novelty and still "how and why" should be central in this work.    [1]
Output: en
El artículo presenta el desarrollo de una solución al problema de analizar tendencias en el desembarco de especies marinas en el borde costero chileno, utilizando herramientas de inteligencia de negocios.  a) aspectos de forma  1. la versión en inglés del título y el resumen del artículo contiene errores gramaticales, de redacción, y de estilo.  2. Uso de mayúsculas: chile --> Chile servicio nacional de pesca --> Servicio Nacional de Pesca kettle --> Kettle tabla 1 --> Tabla 1 api --> API etc.  3. Figura 2 no es referenciada Esta figura muestra --> La Figura 10 muestra  4. parráfo despues de la Figura 1 esta escrito en 1 columna. debe ser en 2 columnas  5. formato no concuerda totalmente con el formato Infonor.  b) aspectos de fondo   1. El artículo no reporta resultados de investigación. Se trata de una aplicación de técnicas de BI para proveer soporte de análisis en línea de datos.  2. Es débil en cuanto a background de procesos y técnicas utilizados. Aunque se listan 12 referencias, solo se citan 2 o 3 de ellas en forma reiterada. No hay revisión de trabajos relacionados (casos de aplicaciones de BI.)  3. Dada la orientación del artículo, debe detallarse mucho más el uso (potencial) de los resultados. En este sentido, debe haber una mayor y mejor interpretación/evaluación por parte de los usuarios de los análisis de la información.
Output: es
This paper applies the idea of normalizing flows (NFs), which allows us to build complex densities with tractable likelihoods, to maximum entropy constrained optimization.  The paper is clearly written and is easy to follow.  Novelty is a weak factor in this paper. The main contributions come from (1) applying previous work on NFs to the problem of MaxEnt estimation and (2) addressing some of the optimization issues resulting from stochastic approximations to E[||T||] in combination with the annealing of Lagrange multipliers. Applying the NFs to MaxEnt is in itself not very novel as a framework. For instance, one could obtain a loss equivalent to the main loss in eq. (6) by minimizing the KLD between KL[p_{\phi};f], where f is the unormalized likelihood f \propto exp \sum_k( - \lambda_k T - c_k ||T_k||^2  ). This type of derivation is typical in all previous works using NFs for variational inference. A few experiments on more complex data would strengthen the paper's results. The two experiments provided show good results but both of them are toy problems.   Minor point:  Although intuitive, it would be good to have a short discussion of step 8 of algorithm 1 as well.
Output: en
The following statement best summarizes the contribution: "This paper shows that model free RL methods can learn how to gather information about physical properties of objects, even when this information is not available to a passive observer, and use this information to make decisions." So this is not a paper about new theory or algorithms, but rather about solving the problem of acquiring knowledge about the physics of the world around us, which is important for many problems and helps explain human performance in many tasks. There are still some concerns about the depth-of-analysis of the paper, but on balance, it is seen as an unconventional but interesting paper. As per AnonReviewer6, the final version could still aim to better address "What should other researchers focus on if they are trying to build agents that can understand physics intuitively (building off this work)?"  -- Area chair
Output: en
This paper takes a model based on that of Graves and retrofits it with a representation derived from the work of Plamondon.  part of the goal of deep learning has been to avoid the use of hand-crafted features and have the network learn from raw feature representations, so this paper is somewhat against the grain.   The paper relies on some qualitative examples as demonstration of the system, and doesn't seem to provide a strong motivation for there being any progress here.  The paper does not provide true text-conditional handwriting synthesis as shown in Graves' original work.   Be more consistent about your bibliography (e.g. variants of Plamondon's own name, use of "et al." in the bibliography etc.)
Output: en
El trabajo presenta el diseño de AMADEUS, una plataforma que permite implementar trayectorias de robots móviles en ambientes semi-estructurados, para la investigación de los algoritmos asociados.  El trabajo en general es adecuado, sin embargo carece de algo importante: la comparación de la plataforma presentada con otras plataformas similares. Esto se ve claramente reflejado en el número de referencias. Sería adecuado el presentar referencias respecto a otras plataformas similares, e indicar cuál es la diferencia de AMADEUS respecto a ellas, lo que dejaría claramente presentado el aporte del trabajo.  Problemas en la redacción del resumen (Para llevar a cabo los movimientos definidos...). También el inglés debe ser revisado.  La calidad de las ecuaciones usadas debe ser mejorada.  La tabla en la página 9 no se ve bien, esto debe ser mejorado.  la sección 10 se llama "resultados", sin embargo lo que muestra son casos de estudio o aplicación de la plataforma.  En resumen, el trabajo es adecuado para ser aceptado, sin embargo el estado del arte respecto a plataformas similares debe ser mejorado.
Output: es
Un muy bien escrito e interesante artículo. Habría sido interesante incluir los algoritmos originales y los propuestos de forma más rigorosa (paso 1, paso 2, etc.) así como un análisis estadístico de la calidad de las respuestas de ambos.   Citar en el texto la figura 4.  Mejorar la calidad de las figuras 6 y 7.   En algunas partes coloca ecuación (n), en otras ec. (n) y en otras ecuación ec. (n)... padronzar para el primero o el segundo.  Padronizar también el nombre de los autores en las referencias bibliográficas, pues algunos usan iniciales, en otro los nombres completos y en otros mixtos.
Output: es
Observación de fondo:  No se presenta un ejemplo de implementación (un ejemplo que sea interesante) de una máquina secuencial mediante la metodolgía propuesta y la tradicional cableada con el fin de establecer comparativos y métricas de desempeño.  Observaciones de forma: realizar las siguientes correcciones:  Omitir el punto final del título en español.  En página 3, segunda columna: "... entrada y las variables de estado de la máquina a secuencial."  En página 4, primera columna: justificación a la izquierda.  En página 5, primera columna: "... cantidad de puertos de entrada y salid ..."  En página 5, primera columna: "... completo a partir de la dirección de cada de cada ...", "... máquina con hasta 15 considerando la suma de ...".  En página 5, segunda columna: "... máquina con hasta 15 considerando la suma de ...".
Output: es
Summary: This paper on autoregressive generative models explores various extensions of PixelCNNs. The proposed changes are to replace the softmax function with a logistic mixture model, to use dropout for regularization, to use downsampling to increase receptive field size, and the introduction of particular skip connections. The authors find that this allows the PixelCNN to outperform a PixelRNN on CIFAR-10, the previous state-of-the-art model. The authors further explore the performance of PixelCNNs with smaller receptive field sizes.  Review: This is a useful contribution towards better tractable image models. In particular, autoregressive models can be quite slow at test time, and the more efficient architectures described here should help with that.  My main criticism regards the severe neglect of related work. Mixture models have been used a lot in autoregressive image modeling, including for multivariate conditional densities and including downsampling to increase receptive field size, albeit in a different manner: Domke (2008), Hosseini et al. (2010), Theis et al. (2012), Uria et al. (2013), Theis et al. (2015). Note that the logistic distribution is a special case of the Gaussian scale mixture (West, 1978).  The main difference seems to be the integration of the density to model integers. While this is clearly a good idea and the right way forward, the authors claim but do not support that not doing this has “proved to be a problem for earlier models based on continuous distributions”. Please elaborate, add a reference, or ideally report the performance achieved by PixelCNN++ without integration (and instead adding uniform noise to make the variables continuous).  60,000 images are not a lot in a high-dimensional space. While I can see the usefulness of regularization for specialized content – and this can serve as a good example to demonstrate the usefulness of dropout – why not use “80 million tiny images” (superset of CIFAR-10) for natural images? Semi-supervised learning should be fairly trivial here (because the model’s likelihood is tractable), so this data could even be used in the class-conditional case.  It would be interesting to know how fast the different models are at test time (i.e., when generating images).
Output: en
el trabajo presenta una posible solución a una problemática real, es interesante la propuesta de la SOS veterinaria. En el trabajo se plantean elementos importantes a tener en cuenta para el desarrollo de una aplicación móvil, aunque en algunas partes del texto es presentada como un desarrollo web. En el trabajo promete el desarrollo de la aplicación. El articulo muestra cómo podría obtenerse una mayor rentabilidad y una mejor gestión organizativa en un SOS Veterinaria, pero no hay un aporte claro que se evidencie en la implementación de un sistema de este tipo.  La figura 2 Process Diagram Level 0 of the Application debe mejorarse, es borrosa. En la figura 3 se presenta la descripción de la solución pero la información debería acompañarse de información que dé cuenta del proceso de forma detallada. hace falta identificar la manera de almacenar y gestionar los datos. La información estará almacenada en una BD? en archivos XML?..... hace falta una pequeña descripción de la arquitectura del sistema. La calidad de las figuras 4 hasta la 12 no es buena, los textos son borrosos.  El artículo debería centrarse en el aporte y los resultados de la aplicación de este tipo de desarrollos.  Se mencionan muchas tecnologías y técnicas de desarrollo pero no es evidente en el desarrollo de la SOS veterinaria.
Output: es
El artículo trata de un proceso de data mining orientado a describir agrupamientos para el análisis de lavados de activos en el rubro bancario. Utiliza como referencia metodológica el proceso CRISP-DM y lo sigue con cierta dedicación.  En cuanto al fondo: detalla demasiados aspectos que guardan relación con la parte descriptiva del tema financiero y del banco en particular donde se circunscribe el desarrollo del trabajo. También enfatiza la obtención de la vista minable que está bien, pero descuida la generación del modelo data mining, específicamente no muestra el modelo de clustering, tampoco los niveles de cobertura y confianza en cada regala de cada clúster, no realiza un análisis estadístico acerca de los patrones y tampoco se observa un validación del modelo y patrones obtenidos. Esto último es la mayor debilidad del trabajo.  En referencia a la forma: hay muchos errores de ortografía y redacción, excesivo el largo de algunos párrafos y redacción de algunos tópicos que se debieran resumir, también redacta en cuarta persona y en algunas partes en tiempo futuro, debiera estandarizar la redacción a 3era persona y tiempo presente, salvo en las conclusiones que puede ocupar tiempo pasado. Excede el nro. de páginas establecidas como máximo. Finalmente, presenta poca bibliografía (sólo 4) y además no las referencias en ningún lado.  Es un artículo que puede ser aceptado pero requiere un re-ordenamiento profundo en cuanto a la forma, y fundamentalmente en relación al modelo de data mining, su análisis y exhaustiva evaluación, así como sus resultados.
Output: es
Revisar la redacción del documento, en varios sitios dice "en base a" debe ser "con base en"  Error tipográfico en la figura 3  En la página 4 antes del subtitulo "Modelo de movimiento" dice del modelo de medida, creo que se refiere al  modelo de medición  Estimo conveniente resaltar cuales son las ventajas del modelo propuesto, tal como esta no es atractivo indican una diferencia con respecto a la referencia entre 7 y 5 cms, esto es  una desviación considerable. Además indica dentro de las desventajas que no es aceptable para trabajar en aplicaciones reales. Sino es así entonces para qué es útil?
Output: es
This paper provides two RNN-based architectures for extractive document summarization. The first, "Classify", reads in the whole document and traverses the sentences a second time to decide whether to include them or not (0/1 decisions). The second, "Select",  reads in the whole document and picks the most relevant sentence one at the time. The models assume that oracle extractive summaries exist, and a pseudo ground-truth generation procedure is used, which mimics Svore et al. (2007) among others.   Overall, this paper seems a small increment over Cheng & Lapata (2016) and performance is similar or worse to that paper. The problem of single document extractive summarization is not particularly exciting since in DUC 2002 (14 years ago) existing models could not beat the lead baseline (which selects the first sentences of the document). It's a pity that this paper doesn't address the most interesting problems of abstractive summarization or apply the proposed approach to multi-document summarization. It's also a little disappointing that the maximum sentence length had to be capped to 50, which suggests the model has some trouble to scale.
Output: en
El artículo describe algunos aspectos generales de la aplicación de minería de datos a redes sociales.  La revisión de información no es completa y dista mucho de ser considerada como el estado del arte del tema.  Sugiero primero revisar artículos de revistas indexadas en el tema, identificar un problema de investigación concreto, donde se pueda realizar un aporte en el tema, y plantear una propuesta de investigación.
Output: es
Considera el uso de tecnologías móviles para incorporar en la medición de material particulado que existe en el interior de una mina. Será interesante ver las ventajas y desventajas de esta aplicación con otras existentes.  Es un estudio solamente. Todavía no hay resultados.
Output: es