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:
EL TEMA QUE ABORDA ESTE TRABAJO ES INTERESANTE. SIN EMBARGO, NO QUEDA CLARO CUÁL ES SU APORTE ORIGINAL. LA ARQUITECTURA PLANTEADA Y LOS DISPOSITIVOS USADOS SON AMPLIAMENTE UTILIZADOS.  COMENTARIOS DE FORMA: 1) Mejorar la redacción, por ejemplo: "Los campos para esta tecnología es muy amplia como lo son juegos..." "...se había comentado en la sección sistema propuesto..." NO EXISTE TAL SECCIÓN. 2) Detallar la descripción del dispositivo Emotiv EEG. ¿Canales en la figura 1? , etc.  RECOMENDACIONES, visitar sitios como: 1) [Link] 2) [Link]

Ex Output:
es


Ex Input:
This work address the problem of supervised learning from strongly labeled data with label noise. This is a very practical and relevant problem in applied machine learning.  The authors note that using sampling approaches such as EM isn't effective, too slow and cannot be integrated into end-to-end training. Thus, they propose to simulate the effects of EM by a noisy adaptation layer, effectively a softmax, that is added to the architecture during training, and is omitted at inference time. The proposed algorithm is evaluated on MNIST and shows improvements over existing approaches that deal with noisy labeled data.  A few comments. 1. There is no discussion in the work about the increased complexity of training for the model with two softmaxes.   2. What is the rationale for having consecutive (serialized) softmaxes, instead of having a compound objective with two losses, or a network with parallel losses and two sets of gradients?  3. The proposed architecture with only two hidden layers isn't not representative of larger and deeper models that are practically used, and it is not clear that shown results will scale to bigger networks.   4. Why is the approach only evaluated on MNIST, a dataset that is unrealistically simple.

Ex Output:
en


Ex Input:
While the overall direction is promising, there are several serious issues with the paper which affect the novelty and validity of the results:  1. Incorrect claims about related work affecting novelty:    - This work is not the first to explore a deep learning approach to automatic code completion: “Toward Deep Learning Software Repositories”, MSR’15 also uses deep learning for code completion, and is not cited.    - “Code Completion with Statistical Language Models”, PLDI’14 is cited incorrectly -- it also does code completion with recurrent neural networks.    - PHOG is independent of JavaScript -- it does representation learning and has been applied to other languages (e.g., Python, see OOPSLA’16 below).     - This submission is not the only one that “can automatically extract features”. Some high-precision (cited) baselines do it.    - “Structured generative models of natural source code” is an incorrect citation. It is from ICML’14 and has more authors. It is also a log-linear model and conditions on more context than claimed in this submission.   2. Uses a non-comparable prediction task for non-terminal symbols: The type of prediction made here is simpler than the one used in PHOG and state-of-the-art (see OOPSLA’16 paper below) and thus the claimed 11 point improvement is not substantiated. In particular, in JavaScript there are 44 types of nodes. However, a PHOG and OOPSLA’16 predictions considers not only these 44 types, but also whether there are right siblings and children of a node. This is necessary for predicting tree fragments instead of a sequence of nodes. It however makes the prediction harder than the one considered here (it leads to 150+ labels, a >3x increase).   3. Not comparing to state-of-the-art: the state-of-the-art however is not the basic PHOG cited here, but “Probabilistic Model for Code with Decision Trees”, (OOPSLA 2016) which appeared before the submission deadline for ICLR’17:

Ex Output:

en