Task: Language Identification
Input (Spanish, English): SUMMARY.  The paper propose a new scoring function for knowledge base embedding. The scoring function called TransGaussian is an novel take on (or a generalization of) the well-known TransE scoring function. The proposed function is tested on two tasks knowledge-base completion and question answering.  ----------  OVERALL JUDGMENT While I think this proposed work is very interesting and it is an idea worth to explore further, the presentation and the experimental section of the paper have some problems. Regarding the presentation, as far as I understand this is not an attention model as intended standardly in the literature. Plus, it has hardly anything to share with memory networks/neural Turing machines, the parallel that the authors try to make is not very convincing. Regarding the experimental section, for a fair comparison the authors should test their model on standard benchmarks, reporting state-of-the-art models. Finally, the paper lack of discussion of results and insights on the behavior of the proposed model.   ----------  DETAILED COMMENTS   In section 2.2 when the authors calculate \mu_{context} do not they loose the order of relations? And if it is so, does it make any sense?
Output (English): en