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 analyzes the story endings (last sentence of a 5-sentence story) in the corpus built for the story cloze task (Mostafazadeh et al. 2016), and proposes a model based on character and word n-grams to classify story endings. The paper also shows better performance on the story cloze task proper (distinguishing between "right" and "wrong" endings) than prior work.  Whereas style analysis is an interesting area and you show better results than prior work on the story cloze task, there are several issues with the paper. First, how do you define "style"? Also, the paper needs to be restructured (for instance, your section "Results" actually mixes some results and new experiments) and clarified (see below for questions/comments): right now, it is quite difficult for the reader to follow what data is used for the different experiments, and what data the discussion refers to.  (1) More details about the data used is necessary in order to assess the claim that "subtle writing task [...] imposes different styles on the author" (lines 729-732). How many stories are you looking at, written by how many different persons? And how many stories are there per person? From your description of the post-analysis of coherence, only pairs of stories written by the same person in which one was judged as "coherent" and the other one as "neutral" are chosen. Can you confirm that this is the case? So perhaps your claim is justified for your "Experiment 1". However my understanding is that in experiment 2 where you compare "original" vs. "right" or "original" vs. "wrong", we do not have the same writers. So I am not convinced lines 370-373 are correct.  (2) A lot in the paper is simply stated without any justifications. For instance how are the "five frequent" POS and words chosen? Are they the most frequent words/POS? (Also theses tables are puzzling: why two bars in the legend for each category?). Why character *4*-grams? Did you tune that on the development set? If these were not the most frequent features, but some that you chose among frequent POS and words, you need to justify this choice and especially link the choice to "style". How are these features reflecting "style"?  (3) I don't understand how the section "Design of NLP tasks" connects to the rest of the paper, and to your results. But perhaps this is because I am lost in what "training" and "test" sets refer to here.  (4) It is difficult to understand how your model differs from previous work. How do we reconcile lines 217-219 ("These results suggest that real understanding of text is required in order to solve the task") with your approach?  (5) The terminology of "right" and "wrong" endings is coming from Mostafazadeh et al., but this is a very bad choice of terms. What exactly does a "right" or "wrong" ending mean ("right" as in "coherent" or "right" as in "morally good")? I took a quick look, but couldn't find the exact prompts given to the Turkers. I think this needs to be clarified: as it is, the first paragraph of your section "Story cloze task" (lines 159-177) is not understandable.  Other questions/comments:  Table 1. Why does the "original" story differ from the coherent and incoherent one? From your description of the corpus, it seems that one Turker saw the first 4 sentences of the original story and was then ask to write one sentence ending the story in a "right" way (or did they ask to provide a "coherent" ending?) and one sentence ending the story in a "wrong" way (or did they ask to provide an "incoherent" ending)? I don't find the last sentence of the "incoherent" story that incoherent... If the only shoes that Kathy finds great are $300, I can see how Kathy doesn't like buying shoes ;-) This led me to wonder how many Turkers judged the coherence of the story/ending and how variable the judgements were. What criterion was used to judge a story coherent or incoherent? Also does one Turker judge the coherence of both the "right" and "wrong" endings, making it a relative judgement? Or was this an absolute judgement? This would have huge implications on the ratings.  Lines 380-383: What does "We randomly sample 5 original sets" mean?  Line 398: "Virtually all sentences"? Can you quantify this?  Table 5: Could we see the weights of the features?   Line 614: "compared to ending an existing task": the Turkers are not ending a "task"  Line 684-686: "made sure each pair of endings was written by the same author" -> this is true for the "right"/"wrong" pairs, but not for the "original"-"new" pairs, according to your description.  Line 694: "shorter text spans": text about what? This is unclear.  Lines 873-875: where is this published?
Output: 

 en