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 considers the problem of decoding diverge solutions from neural sequence models. It basically adds an additional term to the log-likelihood of standard neural sequence models, and this additional term will encourage the solutions to be diverse. In addition to solve the inference, this paper uses a modified beam search.  On the plus side, there is not much work on producing diverse solutions in RNN/LSTM models. This paper represents one of the few works on this topic. And this paper is well-written and easy to follow.  The novel of this paper is relatively small. There has been a lot of prior work on producing diverse models in the area of probailistic graphical models. Most of them introduce an additional term in the objective function to encourage diversity. From that perspective, the solution proposed in this paper is not that different from previous work. Of course, one can argue that most previous work focues on probabilistic graphical models, while this paper focuses on RNN/LSTM models. But since RNN/LSTM can be simply interpreted as a probabilistic model, I would consider it a small novelty.  The diverse beam search seems to straightforward, i.e. it partitions the beam search space into groups, and does not consider the diversity within group (in order to reduce the search space). To me, this seems to be a simple trick. Note most previous work on diverse solutions in probabilistic graphical models usually involve developing some nontrivial algorithmic solutions, e.g. in order to achieve efficiency. In comparison, the proposed solution in this paper seems to be simplistic for a paper.  The experimental results how improvement over previous methods (Li & Jurafsky, 2015, 2016). But it is hard to say how rigorous the comparisons are, since they are based on the authors' own implementation of (Li & Jurasky, 2015, 2016).  --------------- update: given that the authors made the code available (I do hope the code will remain publicly available), this has alleviated some of my concerns about the rigor of the experiments. I will raise my rate to 6.
Output: 

 en