TASK 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].
PROBLEM: 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.

SOLUTION: en

PROBLEM: El artículo presenta un plan de investigación que propone combinar los constructos teóricos de competencias para la gestión de tecnología (con el modelo BTM) y la precepción de éxito profesional (con un modelo propuesto que combina varios factores de diversas fuentes).  La capacidad de gestión de TI es un elemento clave para los profesionales del área y no solo sus competencias tecnológicas. Parece relevante estudiar como impactan estas competencias en el éxito profesional, o, al menos, en la precepción subjetiva de dicho éxito.  El estudio contiene referencias a fuentes relevantes, aunque se sugiere reducir el número y priorizar para el caso de los artículos en revistas o congresos.  Si bien el tema es relevantes existen algunos riesgos en la definición de los constructos que no son analizados. ¿Cuáles son los límites entre la percepción de éxito profesional y personal? ¿Se puede hacer un estudio de la percepción del éxito profesional solo considerando el modelo BTM y dejando fuera las competencias tecnológicas? ¿El estudio aplica a todos los profesionales de TI o solo a los que gestionan proyectos tecnológicos como CIOs?  La principal debilidad de la investigación es que se identifica como un método experimental, cuando en realidad la encuesta no tiene el grado de control sobre el objeto de estudio para clasificarse de esta manera. También se habla de estudio de causalidad, pero la encuesta solo permitiría un estudio de co-relación.  Sobre la encuesta se explica cuáles son los factores evaluados y la escala Lickert. Sin embargo, faltan ejemplos de preguntas concretas y explicar como van a ser respondidas por los sujetos. Por ejemplo: ¿Es viable que una persona evalúe su compromiso o liderazgo? Aunque se esté evaluando la relación en términos generales y no propios, convendría realizar primero una validación del modelo y luego de la propia encuesta con un grupo de expertos o gestores de profesionales de TI.

SOLUTION: es

PROBLEM: This paper explores the ability of nonlinear recurrent neural networks to account for neural response properties that have otherwise eluded the ability of other models.  A multilayer rnn is trained to imitate the stimulus-response mapping measured from actual retinal ganglion cells in response to a sequence of natural images.  The rnn performs significantly better, especially in accounting for transient responses, than conventional LN/GLM models.  This work is an important step in understanding the nonlinear response properties of visual neurons.  Recent results have shown that the responses of even retinal ganglion cells in response to natural movies are difficult to explain in terms of standard receptive field models.  So this presents an important challenge to the field.  If we even had *a* model that works, it would be a starting point.  So this work should be seen in that light.  The challenge now of course is to tease apart what the rnn is doing.  Perhaps it could now be pruned and simplified to see what parts are critical to performance.  It would have been nice to see such an analysis.   Nevertheless this result is a good first start and I think important for people to know about.  I am a bit confused about what is being called a "movie."  My understanding is that it is essentially a sequence of unrelated images shown for 1 sec. each.  But then it is stated that the "frame rate" is 1/8.33 ms.  I think this must refer to the refresh rate of the monitor, right?     I would guess that the deviations from the LN model are even stronger when you show actual dynamic natural scenes - i.e., real movies.  Here I would expect the rnn to have an even more profound effect, and potentially be much more informative.

SOLUTION:
en