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].

[Q]: This work proposes to use visualization of gradients to further understand the importance of features (i.e. pixels) for visual classification. Overall, this presented visualizations are interesting, however, the approach is very ad hoc. The authors do not explain why visualizing regular gradients isn't correlated with the importance of features relevant to the given visual category and proceed to the interior gradient approach.   One particular question with regular gradients at features that form the spatial support of the visual class. Is it the case that the gradients of the features that are confident of the prediction remain low, while those with high uncertainty will have strong gradients?  With regards to the interior gradients, it is unclear how the scaling parameter \alpha affects the feature importance and how it is related to attention.  Finally, does this model use batch normalization?
[A]: en


[Q]: This work describes a gated attention-based recurrent neural network method for reading comprehension and question answering. This method employs a self-matching attention technique to counterbalance the limited context knowledge of gated attention-based recurrent neural networks when processing passages. Finally, authors use pointer networks  with signals from the question attention-based vector to predict the beginning and ending of the answer. Experimental results with the SQuAD dataset offer state-of-the-art performance compared with several recent approaches.   The paper is well-written, structured and explained. As far as I know, the mathematics look also good. In my opinion, this is a very interesting work which may be useful for the question answering community.  I was wondering if the authors have plans to release the code of this approach. From that perspective, I miss a bit of information about the technology used for the implementation (theano, CUDA, CuDNN...), which may be useful for readers.  I would appreciate if authors could perform a test of statistical significance of the results. That would highlight even more the quality of your results.  Finally, I know that the space may be a constraint, but an evaluation including some additional dataset would validate more your work.
[A]: en


[Q]: - Strengths: The paper broadens the applicability of readability scores to an additional language, and produces a well-validated applicability score for Vietnamese.   - Weaknesses: The greatest weaknesses, with respect to ACL are that 1) readability scores are of limited interest within the field of computational linguistics. While they are somewhat useful in educational and public communication fields, their impact on the progress of computational linguistics is limited.  A minor weakness is in the writing: the paper has numerous minor grammatical errors. Although the discussion compares the performance of the PDS1 and PDW1 features from the previous work, it is unclear how poorly the previous readability measures perform, relevant to the one developed here, for practical purposes.  - General Discussion: This paper would be a stronger candidate for inclusion if the corpus (and importantly, labels developed) were released. It could be used more widely than the development of scalar readability metrics, and would enable (e.g.) investigation of application of more powerful feature-selection methods.
[A]:
en