Task: Language Identification
Input (Spanish, English): 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 (English): en