Task: Language Identification
Input (Spanish, English): Some of the key details in this paper are very poorly explained or not even explained at all. The model sounds interesting and there may be something good here, but it should not be published in it's current form.   Specific comments:  The description of the R_l,pi convolutions in Section 2.1 was unclear. Specifically, I wasn't confident that I understood what the labels pi represented.  The description of the SAEN structure in section 2.2 was worded poorly. My understanding, based on Equation 1, is that the 'shift' operation is simply a summation of the representations of the member objects, and that the 'aggregate' operation simply concatenates the representations from multiple relations.  In the 'shift' step, it seems more appropriate to average over the object's member's representations h_j, rather than sum over them.  The compression technique presented in Section 2.3 requires that multiple objects at a level have the same representation. Why would this ever occur, given that the representations are real valued and high-dimensional? The text is unintelligible: "two objects are equivalent if they are made by same sets of parts for all the pi-parameterizations of the R_l,pi decomposition relation."   The 'ego graph patterns' in Figure 1 and 'Ego Graph  Neural Network' used in the experiments are never explained in the text, and no references are given. Because of this, I cannot comment on the quality of the experiments.
Output (English): en