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: This paper describes how to approximate the FastText approach such that its memory footprint is reduced by several orders of magnitude, while preserving its classification accuracy. The original FastText approach was based on a linear classifier on top of bag-of-words embeddings. This type of method is extremely fast to train and test, but the model size can be quite large.  This paper focuses on approximating the original approach with lossy compression techniques. Namely, the embeddings and classifier matrices A and B are compressed with Product Quantization, and an aggressive dictionary pruning is carried out. Experiments on various datasets (either with small or large number of classes) are conducted to tune the parameters and demonstrate the effectiveness of the approach. With a negligible loss in classification accuracy, an important reduction in term of model size (memory footprint) can be achieved, in the order of 100~1000 folds compared to the original size.  The paper is well written overall. The goal is clearly defined and well carried out, as well as the experiments. Different options for compressing the model data are evaluated and compared (e.g. PQ vs LSH), which is also interesting. Nevertheless the paper does not propose by itself any novel idea for text classification. It just focuses on adapting existing lossy compression techniques, which is not necessarily a problem. Specifically, it introduces:   - a straightforward variant of PQ for unnormalized vectors,   - dictionary pruning is cast as a set covering problem (which is NP-hard), but a greedy approach is shown to yield excellent results nonetheless,   - hashing tricks and bloom filter are simply borrowed from previous papers.  These techniques are quite generic and could as well be used in other works.    Here are some minor problems with the paper:    - it is not made clear how the full model size is computed. What is exactly in the model? Which proportion of the full size do the A and B matrices, the dictionary, and the rest, account for? It is hard to follow where is the size bottleneck, which also seems to depend on the target application (i.e. small or large number of test classes). It would have been nice to provide a formula to calculate the total model size as a function of all parameters (k,b for PQ and K for dictionary, number of classes).      - some parts lack clarity. For instance, the greedy approach to prune the dictionary is exposed in less than 4 lines (top of page 5), though it is far from being straightforward. Likewise, it is not clear why the binary search used for the hashing trick would introduce an overhead of a few hundreds of KB.     Overall this looks like a solid work, but with potentially limited impact research-wise.
Output: 

 en