Task: Language Identification
Input (Spanish, English): The paper is clearly written, and the claims are well-supported.  The Related Work in particular is very thorough, and clearly establishes where the proposed work fits in the field.  I had two main questions about the method: (1) phrases are mentioned in section 3.1, but only word representations are discussed.  How are phrase representations derived? (2) There is no explicit connection between M^+ and M^- in the model, but they are indirectly connected through the tanh scoring function.  How do the learned matrices compare to one another (e.g., is M^- like -1*M^+?)?  Furthermore, what would be the benefits/drawbacks of linking the two together directly, by enforcing some measure of dissimilarity?  Additionally, statistical significance of the observed improvements would be valuable.  Typographical comments: - Line 220: "word/phase pair" should be "word/phrase pair" - Line 245: I propose an alternate wording: instead of "entities are translated to," say "entities are mapped to".  At first, I read that as a translation operation in the vector space, which I think isn't exactly what's being described. - Line 587: "slightly improvement in F-measure" should be "slight improvement in F-measure" - Line 636: extraneous commas in citation - Line 646: "The most case" should be "The most likely case" (I'm guessing) - Line 727: extraneous period and comma in citation
Output (English): en