Definition: In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

Positive Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: es

Positive Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: en

Negative Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: en

Negative Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: es

Now complete the following example -
Input: The paper introduces a time dependent recommender system based on point processes parametrized by time dependent user and item latent representations. The later are modeled as coupled – autoregressive processes – i.e. the representation of a user/item changes when he interacts with an item/user, and is a function of both the user and the item representations before time t. This is called coevolution here and the autoregressive process is called recurrent NN. The model may also incorporate heterogeneous inputs. Experiments are performed on several datasets, and the model is compared with different baselines. There are several contributions in the paper: 1) modeling recommendation via parametrized point processes where the parameter dynamics are modeled by latent user/item representations, 2) an optimization algorithm for maximizing the likelihood of this process, with different technical tricks that seem to break its intrinsic complexity, 3) evaluation experiments for time dependent recommendation. The paper by the same authors (NIPS 2016) describes a similar model of continuous time coevolution, and a similar evaluation. The difference lies in the details of the model: the point process model is not the same and of the latent factor dynamic model is slightly different, but the modeling approach and the arguments are exactly the same. By the end, one does not know what makes this model perform better than the one proposed in NIPS, is it the choice for the process, the new parametrization? Both are quite similar. There is no justification on the choice of the specific form of the point process in the two papers. Did the authors tried other forms as well? The same remark applies for the form of the dynamical process: the non-linearity used for the modeling of the latent user/item vectors here is limited to a sigmoid function, which probably does not change much w.r.t. a linear model, but there is no evidence of the role of this non linearity in the paper. Note that there are some inconsistencies between the results in the two papers. Concerning the evaluation, the authors introduce two criteria. I did not get exactly how they evaluate the item recommendation: it is mentioned that at each time t, the model predicts the item the user will interact with. Do you mean, the next item the user will interact with after time t? For the time prediction, why is it a relevant metric for recommendation? A comparison of the complexity, or execution time of the different methods would be helpful. The complexity of your method is apparently proportional to #items*#users, what are the complexity limits of your methods. Overall, the paper is quite nice and looks technically sound, albeit many details are missing. On the other hand, I have a mixed feeling because of the similarity with NIPS paper. The authors should have make a better work at convincing us that this is not a marginal extension of previous work by the authors.  I was not convinced either by the evaluation criteria and there is no evidence that the model can be used for large datasets.
Output: 

 en