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].

Ex Input:
This paper describes a new deterministic dependency parsing algorithm and analyses its behaviour across a range of languages. The core of the algorithm is a set of rules defining permitted dependencies based on POS tags. The algorithm starts by ranking words using a slightly biased PageRank over a graph with edges defined by the permitted dependencies. Stepping through the ranking, each word is linked to the closest word that will maintain a tree and is permitted by the head rules and a directionality constraint.  Overall, the paper is interesting and clearly presented, though seems to differ only slightly from Sogaard (2012), "Unsupervised Dependency Parsing without Training". I have a few questions and suggestions:  Head Rules (Table 1) - It would be good to have some analysis of these rules in relation to the corpus. For example, in section 3.1 the fact that they do not always lead to a connected graph is mentioned, but not how frequently it occurs, or how large the components typically are.  I was surprised that head direction was chosen using the test data rather than training or development data. Given how fast the decision converges (10-15 sentences), this is not a major issue, but a surprising choice.  How does tie-breaking for words with the same PageRank score work? Does it impact performance significantly, or are ties rare enough that it doesn't have an impact?  The various types of constraints (head rules, directionality, distance) will lead to upper bounds on possible performance of the system. It would be informative to include oracle results for each constraint, to show how much they hurt the maximum possible score. That would be particularly helpful for guiding future work in terms of where to try to modify this system.  Minor:  - 4.1, "we obtain [the] rank"  - Table 5 and Table 7 have columns in different orders. I found the Table 7 arrangement clearer.  - 6.1, "isolate the [contribution] of both"

Ex Output:
en


Ex Input:
The paper describes a network architecture for inverse problems in computer vision. Example inverse problems considered are image inpainting, computing intrinsic image decomposition and foreground/background separation. The architecture is composed of (i) a generator that produces target (latent) output (such as foreground / background regions),  (ii) renderer that composes that latent output back to the image that can be compared with the input to measure reconstruction error,  and (iii) adversarial prior that ensures the target output (latent) image respects a certain image statistics.  Strong  points. - The proposed architecture with memory database is interesting and appears to be novel.   Weak points: - Experimental results are only proof-of-concept in toy set-ups and do not clearly demonstrate benefits of the proposed architecture. - It is unclear whether the memory retrieval engine that retrieves images based on L2 distance on pixel values is going generalize to other more realistic scenarios.  - Clarity. The clarity of explanation can be also improved (see below).   Detailed evaluation.  Originality: - The novelty of this work lies in the (iii) adversarial prior that places an adversarial loss between the generated latent output and a single image retrieved from a large unlabelled database of target output examples (called memory). The adversarial prior has a convolutional form matching local image statistics, rather than the entire image.  The particular form of network architecture with the memory-based fully convolutional adversarial loss appears to be novel and potentially interesting.  - Motivation for the Architecture. The weakest point of the proposed architecture is the "Memory retrieval engine" R (section 2.4), where images are retrieved from the memory by measuring L2 distance on pixel intensities. While this maybe ok for simple problems considered in this work, it is unclear how this can generalize to other more complicated datasets and problems.   This should be better discussed, better justified and ideally results in some more realistic set-up shown (see below).   Quality: - Experiments. Results are shown for inpainting of MNIST digits, intrinsic image decomposition on the MIT intrinsic image database, and figure/ground layer extraction on the synthesized dataset of 3D chairs rendered onto background from real photographs.    The experimental validation of the model is not very strong and proof-of-concept only. All the experiments are performed in simplified toy set-ups. The MNIST digit inpainting is far from current state-of-the-art on image inpainting in real photographs (see e.g. Pathak et al., 2016). The foreground background separation is done on  only synthetically generated test data. Even for intrinsic image demposition problem there is now relatively large-scale dataset of (Bell et al., 2014), see the citation below.    While this is probably ok for the ICLR paper, it diminishes the significance of the work. Is this model going to be useful in a real settings? One possibility to address this would be to focus on one of the problems and show results on a challenging state-of-the-art data. It would be great to see the benefits of the memory database.   S. Bell, K. Bala, and N. Snavely. Intrinsic images in the wild. ACM Transactions on Graphics, 33(4):159, 2014.  Clarity: - The clarity of the writing can be improved. I found some of the terminology of the paper, specially the “imagination” and “memory” confusing. From figure 2, it is not clear how the “memories” for the given input image are obtained, which also took me some time to understand.  - To help understand the proposed architecture, it would be useful to draw an illustration of what is happening in the "feature space”, similar in spirit e.g. to figure 2 in

Ex Output:
en


Ex Input:
El artículo trata sobre la aplicación de BPMN para la comprensión de un proceso de negocio en una empresa minera de Perú.  El documento se encuentra bien estructurado y su lectura es fácil. Se observan pocos errores de redacción. Además su formato es el exigido por el congreso.  En relación al tema del artículo, al ser un aplicación de BPMN, no es novedoso. A partir del caso, creo que era posible realizar un mayor aporte, quizás proponiendo una metodología para abordar la revisión de procesos de negocios, considerando que los actores internos y externos de la empresa, generalmente priorizan la optimización de sus procesos productivos más que la revisión de otros procesos. Por último y no menos importante, el énfasis del artículo es la revisión (auditoría) del proceso con BPMN, alejándose de las temáticas del congreso. Se podría dar mayor énfasis aplicaciones asociados al congreso, tales como análisis de requerimientos a partir de BPMN, implementación de SOA desde BPMN, monitoreo de procesos con BAM y BPMN, etc.  En algunas figuras, a pesar que se mencionan a los contratistas como una entidad externa, no se utiliza un “lane” para representarlos y así observar la interacción con el proceso en revisión.  El trabajo futuro planteado está relacionado con los resultados obtenidos de las mejoras implementadas a partir del análisis realizado en este trabajo. Creo que más que seguir las mejoras implementadas es conveniente hacer un seguimiento a la forma en que se aborda la revisión de un procesos en un ambiente que no prioriza este tipo de tareas o es complejo de realizarlas.

Ex Output:

es