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: - Summary:   The paper introduces a new dataset for a sarcasm interpretation task and a system (called Sarcasm SIGN) based on machine translation framework Moses. The new dataset was collected from 3000 sarcastic tweets (with hashtag `#sarcasm) and 5 interpretations for each from humans. The Sarcasm SIGN is built based on Moses by replacing sentimental words by their corresponding clusters on the source side (sarcasm) and then de-cluster their translations on the target side (non-sarcasm). Sarcasm SIGN performs on par with Moses on the MT evaluation metrics, but outperforms Moses in terms of fluency and adequacy.   - Strengths:  the paper is well written  the dataset is collected in a proper manner  the experiments are carefully done and the analysis is sound.  - Weaknesses:  lack statistics of the datsets (e.g. average length, vocabulary size)  the baseline (Moses) is not proper because of the small size of the dataset  the assumption "sarcastic tweets often differ from their non sarcastic interpretations in as little as one sentiment word" is not supported by the data.   - General Discussion: This discussion gives more details about the weaknesses of the paper.   Half of the paper is about the new dataset for sarcasm interpretation. However, the paper doesn't show important information about the dataset such as average length, vocabulary size. More importantly, the paper doesn't show any statistical evidence to support their method of focusing on sentimental words.   Because the dataset is small (only 3000 tweets), I guess that many words are rare. Therefore, Moses alone is not a proper baseline. A proper baseline should be a MT system that can handle rare words very well. In fact, using clustering and declustering (as in Sarcasm SIGN) is a way to handle rare words.  Sarcasm SIGN is built based on the assumption that "sarcastic tweets often differ from their non sarcastic interpretations in as little as one sentiment word". Table 1 however strongly disagrees with this assumption: the human interpretations are often different from the tweets at not only sentimental words. I thus strongly suggest the authors to give statistical evidence from the dataset that supports their assumption. Otherwise, the whole idea of Sarcasm SIGN is just a hack.  --------------------------------------------------------------  I have read the authors' response. I don't change my decision because of the following reasons:   - the authors wrote that "the Fiverr workers might not take this strategy": to me it is not the spirit of corpus-based NLP. A model must be built to fit given data, not that the data must follow some assumption that the model is built on.  - the authors wrote that "the BLEU scores of Moses and SIGN are above 60, which is generally considered decent in the MT literature": to me the number 60 doesn't  show anything at all because the sentences in the dataset are very short. And that, if we look at table 6, %changed of Moses is only 42%, meaning that even more than half of the time translation is simply copying, the BLUE score is more than 60.  - "While higher scores might be achieved with MT systems that explicitly address rare words, these systems don't focus on sentiment words": it's true, but I was wondering whether sentiment words are rare in the corpus. If they are, those MT systems should obviously handle them (in addition to other rare words).
Output: 

 en