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 interesting and ambitious work: the automated conversion of Universal Dependency grammar structures into [what the paper calls] semantic logical form representations.  In essence, each UD construct is assigned a target construction in logical form, and a procedure is defined to effect the conversion, working ‘inside-out’ using an intermediate form to ensure proper nesting of substructures into encapsulating ones.  Two evaluations are carried out: comparing the results to gold-standard lambda structures and measuring the effectiveness of the resulting lambda expressions in actually delivering the answers to questions from two QA sets.    It is impossible to describe all this adequately in the space provided.  The authors have taken some care to cover all principal parts, but there are still many missing details.  I would love to see a longer version of the paper!  Particularly the QA results are short-changed; it would have been nice to learn which types of question are not handled, and which are not answered correctly, and why not.  This information would have been useful to gaining better insight into the limitations of the logical form representations.    That leads to my main concern/objection.  This logical form representation is not in fact a ‘real’ semantic one.                          It is, essentially, a rather close rewrite of the dependency structure of the input, with some (good) steps toward ‘semanticization’, including the insertion of lambda operators, the explicit inclusion of dropped arguments (via the enhancement operation), and the introduction of appropriate types/units for such constructions as eventive adjectives and nouns like “running horse” and “president in 2009”.  But many (even simple) aspects of semantic are either not present (at least, not in the paper) and/or simply wrong.  Missing: quantification (as in “every” or “all”); numbers (as in “20” or “just over 1000”); various forms of reference (as in “he”, “that man”, “what I said before”); negation and modals, which change the semantics in interesting ways; inter-event relationships (as in the subevent relationship between the events in “the vacation was nice, but traveling was a pain”; etc. etc.  To add them one can easily cheat, by treating these items as if they were just unusual words and defining obvious and simple lambda formulas for them.  But they in fact require specific treatment; for example, a number requires the creation of a separate set object in the representation, with its own canonical variable (allowing later text to refer to “one of them” and bind the variable properly).  For another example, Person A’s model of an event may differ from Person B’s, so one needs two representation symbols for the event, plus a coupling and mapping between them.  For another example, one has to be able to handle time, even if simply by temporally indexing events and states.  None of this is here, and it is not immediately obvious how this would be added.  In some cases, as DRT shows, quantifier and referential scoping is not trivial.    It is easy to point to missing things, and unfair to the paper in some sense; you can’t be expected to do it all.  But you cannot be allowed to make obvious errors.  Very disturbing is the assignment of event relations strictly in parallel with the verb’s (or noun’s) syntactic roles.  No-one can claim seriously that “he broke the window” and “the window broke” has “he” and “the window” filling the same semantic role for “break”.  That’s simply not correct, and one cannot dismiss the problem, as the paper does, to some nebulous subsequent semantic processing.                          This really needs adequate treatment, even in this paper.  This is to my mind the principal shortcoming of this work; for me this is the make-or-break point as to whether I would fight to have the paper accepted in the conference.  (I would have been far happier if the authors had simply acknowledged that this aspect is wrong and will be worked on in future, with a sketch saying how: perhaps by reference to FrameNet and semantic filler requirements.)                            Independent of the representation, the notation conversion procedure is reasonably clear.  I like the facts that it is rather cleaner and simpler than its predecessor (based on Stanford dependencies), and also that the authors have the courage of submitting non-neural work to the ACL in these days of unbridled and giddy enthusiasm for anything neural.
Output: 

 en