instruction:
In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
question:
This paper presents an approach to tag word senses with temporal information (past, present, future or atemporal). They model the problem using a graph-based semi-supervised classification algorithm that allows to combine item specific information - such as the presence of some temporal indicators in the glosses - and the structure of Wordnet - that is semantic relations between synsets â, and to take into account unlabeled data. They perform a full annotation of Wordnet, based on a set of training data labeled in a previous work and using the rest of Wordnet as unlabeled data. Specifically, they take advantage of the structure of the label set by breaking the task into a binary formulation (temporal vs atemporal), then using the data labeled as temporal to perform a finer grained tagging (past, present or future). In order to intrinsically evaluate their approach, they annotate a subset of synsets in Wordnet using crowd-sourcing. They compare their system to the results obtained by a state-of-the-art time tagger (Stanford's SUTime) using an heuristic as a backup strategy, and to previous works. They obtain improvements around 11% in accuracy, and show that their approach allows performance higher than previous systems using only 400 labeled data. Finally, they perform an evaluation of their resource on an existing task (TempEval-3) and show improvements of about 10% in F1 on 4 labels.  This paper is well-constructed and generally clear, the approach seems sound and well justified. This work led to the development of a resource with fine grained temporal information at the word sense level that would be made available and could be used to improve various NLP tasks. I have a few remarks, especially concerning the settings of the experiments.  I think that more information should be given on the task performed in the extrinsic evaluation section. An example could be useful to understand what the system is trying to predict (the features describe âentity pairsâ but it has not been made clear before what are these pairs) and what are the features (especially, what are the entity attributes? What is the POS for a pair, is it one dimension or two? Are the lemmas obtained automatically?). The sentence describing the labels used is confusing, I'm not sure to understand what âevent to document creation timeâ and âevent to same sentence eventâ means, are they the kind of pairs considered? Are they relations (as they are described as relation at the beginning of p.8)? I find unclear the footnote about the 14 relations: why the other relations have to be ignored, what makes a mapping too âcomplexâ? Also, are the scores macro or micro averaged? Finally, the ablation study seems to indicate a possible redundancy between Lexica and Entity with quite close scores, any clue about this behavior?  I have also some questions about the use of the SVM.  For the extrinsic evaluation, the authors say that they optimized the parameters of the algorithm: what are these parameters?  And since a SVM is also used within the MinCut framework, is it optimized and how? Finally, if it's the LibSVM library that is used (Weka wrapper), I think a reference to LibSVM should be included.   Other remarks: - It would be interesting to have the number of examples per label in the gold data, the figures are given for coarse grained labels (127 temporal vs 271 atemporal), but not for the finer grained. - It would also be nice to have an idea of the number of words that are ambiguous at the temporal level, words like âpresentâ. - It is said in the caption of the table 3 that the results presented are âsignificantly betterâ but no significancy test is indicated, neither any p-value.  Minor remarks: - Related work: what kind of task was performed in (Filannino and Nenadic, 2014)? - Related work: ârequires a post-calibration procedureâ, needs a reference (and p.4 in 3.3 footnote it would be clearer to explain calibration) - Related work: âtheir model differ from oursâ, in what? - Table 3 is really too small: maybe, remove the parenthesis, put the â(p,r,f1)â in the caption and give only two scores, e.g. prec and f1. The caption should also be reduced. - Information in table 4 would be better represented using a graph. - Beginning of p.7: 1064 â 1264 - TempEval-3: reference ? - table 6: would be made clearer by ordering the scores for one column - p.5, paragraph 3: atemporal) â atemporal
answer:
en


question:
El paper describe el proceso de evaluación de una aplicación móvil a partir de un conjunto de índices pre-definidos. En las dos primeras páginas se expone el tipo de aplicación móvil y la justificación a la selección de índices. Luego un análisis de los índices, indicando la muestra realizada y la metodología.  * Se extraña una presentación formal de la aplicación móvil, más allá de simplemente expresar su funcionalidad. * La búsqueda de correlaciones entre los distintos índices son obvias y están asociadas claramente a la evaluación por parte de los usuarios. * Desconozco la necesidad de plantear la diferencia entre sexo de la población muestral, no es un dato significativo. * El cuestionario seleccionado * Es poco significativo el encontrar a partir de los datos que la variable que más aporta a la predicción de calidad es la "usabilidad". Este punto es obvio en una aplicación móvil debido a su naturaleza. * Las conclusiones no aportan al trabajo realizado, se debería plantear una posible extensión de la evaluación a dispositivos móviles de tamaño mayor, por ejemplo: tables.  El proceso de medición de calidad que se propone sólo puede ser evaluado en su aplicación en distintas aplicaciones móviles, no solamente en una para mejorar la gestión de pago de los impuestos federales.  Con respecto a la ortografía, es muy común la falta de tilde en varias palabras: solicitó, explicó,  especificó, elaboró, proporcionó, etc.
answer:
es


question:
The paper presents an interesting incremental approach for exploring new convolutional network hierarchies in an incremental manner after a baseline network has reached a good recognition performance.  The experiments are presented for the CIFAR-100 and ImageNet benchmarks by morphing various ResNet models into better performing models with somewhat more computation.  Although the baselines are less strong than those presented in the literature, the paper claims significant error reduction for both ImageNet and CIFAR-100.  The main idea of the paper is to rewrite convolutions into multiple convolutions while expanding the number of filters. It is quite unexpected that this approach yields any improvements over the baseline model at all.  However, for some of the basic tenets of network morphing, experimental evidence is not given in the paper. Here are some fundamental questions raised by the paper: - How does the quality of morphed networks compares to those with the same topology trained from scratch? - How does the incremental training time after morphing relate to that of the network trained from scratch? - Where is the extra computational cost of the morphed networks come from? - Why is the quality of the baseline ResNet models lag behind those that are reported in the literature and github? (E.g. the github ResNet-101 model is supposed to have 6.1% top-5 recall vs 6.6 reported in the paper) More evidence for the first three points would be necessary to evaluate the validity of the claims of the paper.  The paper is written reasonably well and can be understood quite well, but the missing evidence and weaker baselines make it looks somewhat less convincing.  I would be inclined to revise up the score if a more experimental evidence were given for the main message of the paper (see the points above).
answer:

en