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