Task: Language Identification
Input (Spanish, English): The paper presents a layer architecture where a single parameter is used to  gate the output response of layer to amplify or suppress it. It is shown that such an architecture can ease optimization of a deep network as it is easy to learn identity mappings in layers helping in better gradient propagation to lower layers (better supervision).   Using an introduced SDI metric it shown that gated residual networks can most easily learn identity mappings compared to other architectures.   Although good theoretical reasoning is presented the observed experimental evidence of learned k values does not seem to strongly support the theory given that learned  k values are mostly very small and not varying much across layers. Also, experimental validation of the approach is not quite strong in terms of reported performances and number of large scale experiments.
Output (English): en