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 Q]: 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.
[EX A]: en

[EX Q]: - El tema de adopcion de sistemas ERP se ha desarrollado los ultimos 15 anos, y en particular la revision de factores que impliquen su exito es un tema de investigacion relevante. - En el trabajo se desarrollo un analisis de la literatura ERP sobre el tema que aborda factores criticos de exito, metodologias de adopcion y tamano de la organizacion. - Si bien es un error indicar que "ultimamente se ha generado un tercer tipo..." en relacion a asociar fases del proyecto y FCE, pues hace mas de 12 anos esto se ha realizado, ver Cita [36] del mismo trabajo, no es un tema cerrado. - Propongo a los autores el definir que es "exito de adopcion ERP", pues existe literatura en relacion a ello muy interesante para sus fines ultimos (ver trabajos de la profesora Markus). - Metodologicamente existen tres limitaciones importantes a superar con miras a avanzar en una publicacion cientifica de impacto. Primero, no se ha considerado el factor de impacto de la publicaciones, con lo cual se ha desestimado el desarrollo asociado a este tipo de revisiones, mezclando trabajos de alto impacto (ejemplo, articulos de revistas ISIs factor de impacto >2, como MISQ) con trabajos sin una revision de calidad (ejemplo, lo que Scholar "lanza" solo por coincidir en la busqueda). Segundo, se dice que se tomaran referencias del 2008 en adelante prioritariamente, pero muchas de sus referencias son de antes de esa fecha, y eso no es raro, pues entre 2001 y 2004 hay una explosion de trabajos en este ambito especifico. Tercero, no se han incluido explicitamente trabajos de conferencias del area de alto impacto en la comunidad cientifica como AMCIS o ECIS (la primera tiene una base de datos privada, no accesible por google y la segunda es libre). - Las limitaciones antes expuestas restringen los alcances de los interesantes resultados expuestos en el trabajo.
[EX A]: es

[EX Q]: - Strengths: A nice, solid piece of work that builds on previous studies in a productive way. Well-written and clear. - Weaknesses: Very few--possibly avoid some relatively "empty" statements: 191 : For example, if our task is to identify words used similarly across contexts, our scoring function can be specified to give high scores to terms whose usage is similar across the contexts. 537 : It is educational to study how annotations drawn from the same data are similar or different. - General Discussion: In the first sections I was not sure that much was being done that was new or interesting, as the methods seemed very reminiscent of previous methods used over the past 25 years to measure similarity, albeit with a few new statistical twists, but conceptually in the same vein. Section 5, however, describes an interesting and valuable piece of work that will be useful for future studies on the topic. In retrospect, the background provided in sections 2-4 is useful, if not necessary, to support the experiments in section 5. In short, the work and results described will be useful to others working in this area, and the paper is worthy of presentation at ACL. Minor comments: Word, punctuation missing? 264 : For word annotations, we used PPMI, SVD, and SGNS (skipgram with negative sampling from Mikolov et al. (2013b)) word vectors released by Hamilton et al. (2016). Unclear what "multiple methods" refers to : 278 : some words were detected by multiple methods with CCLA
[EX A]:
en