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 clear hierarchical taxonomy of transfer learning methods as applicable to sequence tagging problems. This contextualizes and unifies previous work on specific instances of this taxonomy. Moreover, the paper shows that previously unexplored places in this taxonomy are competitive with or superior to the state of the art in key benchmark problems.  It'd be nice to see this explored further, such as highlighting what is the loss as you move from the more restrictive to the less restrictive transfer learning approaches, but I believe this paper is interesting and acceptable as-is.

Output: en


Input: Consider Input: This paper extends preceding works to create a mapping between the word embedding space of two languages. The word embeddings had been independently trained on monolingual data only, and various forms of bilingual information is used to learn the mapping. This mapping is then used to measure the precision of translations.  In this paper, the authors propose two changes: "CCA" and "inverted softmax".  Looking at Table 1, CCA is only better than Dina et al in 1 out of 6 cases (It/En @1).  Most of the improvements are in fact obtained by the introduction of the inverted softmax normalization.  Overall, I wonder which aspect of this paper is really new. You mention:  - Faruqui & Dyer 2014 already used CCA and dimensionality reduction  - Xing et al 2015 argued already that Mikolov's linear matrix should be orthogonal  Could you make clear in what aspect your work is different from Faruqui & Dyer 2014 (other the fact that you applied the method to measure translation precision) ?  Using cognates instead of a bilingual directory is a nice trick. Please explain how you obtained this list of cognates ? Obviously, this only works for languages with the same alphabet (for instance Greek and Russian are excluded)  Also, it seems to me that in linguistics the term "cognate" refers to words which have a common etymological origin - they don't necessarily have the same written form (e.g. night, nuit, noche, Nacht). Maybe, you should use a different term ? Those words are probably proper names in news texts.

Output: en


Input: Consider Input: El artículo se deja leer con facilidad, en especial en su primera mitad. Luego, hay errores de escritura y redacción que van afectando la comprensión de lo realizado (PostgrSQL en vez de PostgresSQL, con tiene en vez de contiene, ...). Adicionalmente se detallan pasos, que caen en detalles irrelevantes, como indicar que debe instalarse tal o cual herramienta, sin entrar en los aspectos realmente importantes, especialmente en la implementación del acoplamiento medio (donde, no se detalla que se implementó a nivel del SABD y que a nivel externo). Lo más criticable es el uso de dos SABD (PostgreSQL para el acoplamiento débil, y Oracle para los acoplamientos medio y fuerte) en la experimentación, esto hace que sea muy discutible la comparación que, ya no sólo tienen la implementación del algoritmo como variable independiente, sino también el SABD a utilizar.  Pse a lo anterior, pienso que el trabajo es digno de ser presentado, de forma de mostrar, en particular, las diferentes alternativas de integración de algoritmos de Minería de Datos con Sistemas Administradores de Bases de Datos.


Output: es