Task: Language Identification
Input (Spanish, English): - Strengths:  -- A well-motivated approach, with a clear description and solid results.  - Weaknesses:  -- Nothing substantial other than the comments below.   - General Discussion:  The paper describes a new method called attention-over-attention for reading comprehension. First layers of the network compute a vector for each query word and document word, resulting in a |Q|xK matrix for the query and a |D|xK for the document. Since the answer is a document word, an attention mechanism is used for assigning weights to each word, depending on their interaction with query words. In this work, the authors deepen a traditional attention mechanism by computing a weight for each query word through a separate attention and then using that to weight the main attention over document words. Evaluation is properly conducted on benchmark datasets, and various insights are presented through an analysis of the results as well as a comparison to prior work. I think this is a solid piece of work on an important problem, and the method is well-motivated and clearly described, so that researchers can easily reproduce results and apply the same techniques to other similar tasks.  - Other remarks:  -- p4, Equation 12: I am assuming i is iterating over training set and p(w) is referring to P(w|D,Q) in the previous equation? Please clarify to avoid confusion.  -- I am wondering whether you explored/discussed initializing word embeddings with existing vectors such as Google News or Glove? Is there a reason to believe the general-purpose word semantics would not be useful in this task?  -- p6 L589-592: It is not clear what the authors are referring to when they say 'letting the model explicitly learn weights between individual attentions'? Is this referring to their own architecture, more specifically the GRU output indirectly affecting how much attention will be applied to each query and document word? Clarifying that would be useful. Also, I think the improvement on validation is not 4.1, rather 4.0 (72.2-68.2).  -- p7 Table 5: why do you think the weight for local LM is relatively higher for the CN task while the benefit of adding it is less? Since you included the table, I think it'll be nice to provide some insights to the reader.  -- I would have liked to see the software released as part of this submission.  -- Typo p2 L162 right column: "is not that effective than expected" --> "is not as effective as expected"?  -- Typo p7 L689 right column: "appear much frequent" --> "appears more frequently"?  -- Typo p8 L719-721 left column: "the model is hard to" --> "it is hard for the model to"? & "hard to made" --> "hard to make"?
Output (English): en