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].

Input: Consider Input: This paper presents a set of experiments investigating what kinds of information are captured in common unsupervised approaches to sentence representation learning. The results are non-trivial and somewhat surprising. For example, they show that it is possible to reconstruct word order from bag of words representations, and they show that LSTM sentence autoencoders encode interpretable features even for randomly permuted nonsense sentences.  Effective unsupervised sentence representation learning is an important and largely unsolved problem in NLP, and this kind of work seems like it should be straightforwardly helpful towards that end. In addition, the experimental paradigm presented here is likely more broadly applicable to a range of representation learning systems. Some of the results seem somewhat strange, but I see no major technical concerns, and think that that they are informative. I recommend acceptance.  One minor red flag:  - The massive drop in CBOW performance in Figures 1b and 4b are not explained, and seem implausible enough to warrant serious further investigation. Can you be absolutely certain that those results would appear with a different codebase and different random seed implementing the same model? Fortunately, this point is largely orthogonal to the major results of the paper.  Two writing comments: - I agree that the results with word order and CBOW are surprising, but I think it's slightly misleading to say that CBOW is predictive of word order. It doesn't represent word order at all, but it's possible to probabilistically reconstruct word order from the information that it does encode. - Saying that "LSTM auto-encoders are more effective at encoding word order than word content" doesn't really make sense. These two quantities aren't comparable.

Output: en


Input: Consider Input: Fondo: - Se menciona de manera muy superficial el lenguaje a utilizar en forma posterior a Scratch, quizás no es necesario o se debe ahondar más en el tema. - El número de alumnos sobre el cual se aplicó el estudio es muy pequeño para obtener resultados concluyentes. - Sería deseable una comparación o estudio entre la utilización de Scratch y Alice (mencionado en el trabajo). - Concluir a partir del experimento que Scratch es una herramienta adecuada para la educación en Chile, sin incluir resultados con otros lenguajes es un poco apresurado. - Sería deseable mencionar que instituciones o trabajos relacionados con lenguajes para fomentar el pensamiento lógico se han realizado en Chile. No se menciona ninguno como referencia o en la bibliografía. - El ejercicio utilizado en el experimento es uno sólo, se deberían utilizar más si se desea objetivamente evaluar su utilización como lenguaje para mejorar el razonamiento lógico. - La primera cita proviene de un portal de noticias Colombiano y del 2006. Sería deseable una referencia más sólida y local asociada quizás a los lineamientos y planes de estudio del ministerio de educación.   Forma: - Se debe re-escribir el abstract: posee muchas palabras separadas por comas y la redacción incluye ideas desconectadas entre sí. - Se cita 8 veces la cita 7/8. Con la mitad de ellas queda clara la referencia. - ".. pero pocos aprender a programar .." debería ser: ".. pero pocos aprendeN a programar ..". - La cita 2 dice .. "Fundamentos de POgramacion".. debería ser: ".. Fundamentos de Programación".

Output: es


Input: Consider Input: This paper introduces UCCA as a target representation for semantic parsing and also describes a quite successful transition-based parser for inference into that representation. I liked this paper a lot. I believe there is a lot of value simply in the introduction of UCCA (not new, but I believe relatively new to this community), which has the potential to spark new thinking about semantic representations of text. I also think the model was well thought out. While the model itself was fairly derivative of existing transition-based schemes, the extensions the authors introduced to make the model applicable in this domain were reasonable and well-explained, at what I believe to be an appropriate level of detail.  The empirical evaluation was pretty convincing -- the results were good, as compared to several credible baselines, and the authors demonstrated this performance in multiple domains. My biggest complaint about this paper is the lack of multilingual evaluation, especially given that the formalism being experimented with is exactly one that is supposed to be fairly universal. I'm reasonably sure multilingual UCCA corpora exist (in fact, I think the "20k leagues" corpus used in this paper is one such), so it would be good to see results in a language other than English.  One minor point: in section 6, the authors refer to their model as "grammarless", which strikes me as not quite correct. It's true that the UCCA representation isn't derived from linguistic notions of syntax, but it still defines a way to construct a compositional abstract symbolic representation of text, which to me, is precisely a grammar. (This is clearly a quibble, and I don't know why it irked me enough that I feel compelled to address it, but it did.)  Edited to add: Thanks to the authors for their response.


Output: en