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].

Ex Input:
A few issues with this paper: 1- I find finding #2 trivial and unworthy of mention, but the author don't seem to agree with me that it is. See discussions. 2- Finding #1 relies on Fig #4, which appears very noisy and doesn't provide any error analysis. It makes me question how robust this finding is. One would have naively expected the power usage trend to mirror Fig #3, but given the level of noise, I can't convince myself whether the null hypothesis of there being no dependency between batch size and power consumption is more likely than the alternative. 3- Paper is unfriendly to colorblind readers (or those with B/W printers)  Overall, this paper is a reasonable review of where we are in terms of SOTA vision architectures, but doesn't provide much new insight. I found most interesting the clear illustration that VGG models stand out in terms of being a bad tradeoff in resource-constrained environments (too many researchers are tempted to benchmark their model compression algorithm on VGG-class models because that's always where one can show 10x improvements without doing much.)

Ex Output:
en


Ex Input:
- Strengths: This paper presents an extension of many popular methods for learning vector representations of text.  The original methods, such as skip-gram with negative sampling, Glove, or other PMI based approaches currently use word cooccurrence statistics, but all of those approaches could be extended to n-gram based statistics.  N-gram based statistics would increase the complexity of every algorithm because both the vocabulary of the embeddings and the context space would be many times larger.  This paper presents a method to learn embeddings for ngrams with ngram context, and efficiently computes these embeddings.  On similarity and analogy tasks, they present strong results.  - Weaknesses: I would have loved to see some experiments on real tasks where these embeddings are used as input beyond the experiments presented in the paper.  That would have made the paper far stronger.  - General Discussion: Even with the aforementioned weakness, I think this is a nice paper to have at ACL.  I have read the author response.

Ex Output:
en


Ex Input:
El artículo expone el desarrollo de un caso de estudio de reconocimiento de patrones para identificar remolinos oceánicos por medio de imágenes de satélite.  El articulo está bien escrito y estructurado. Su presentación sigue una secuencia lógica de desarrollo.  En lo que corresponde al estado del arte, se mencionan métodos para la detección de "eddies". Salvo el caso de redes neuronales, no se mencionan resultados cuantitativos obtenidos con cada método.  Por lo mismo, no es posible establecer una comparación del rendimiento obtenido versus otros métodos referenciados en el estado del arte.  La parte de entendimiento y exploración de los datos es débil.

Ex Output:

es