text: This paper tackles the task of learning embeddings of multi-relational graphs using a neural network. As much of previous work, the proposed architecture works on triples (h, r, t) wth h, t entities and r the relation type. Despite interesting experimental results, I find that the paper carries too many imprecisions as is. * One of the main originality of the approach is to be able for a given input triple to train by sequentially removing in turn the head h, then the tail t and finally the relation r. (called multi-shot in the paper). However, most (if not all) approaches learning embeddings of multi-relational graphs also create multiple examples given a triple. And that, at least since "Learning Structured Embeddings of Knowledge Bases" by Bordes et al. 2011 that was predicting h and t (not r). The only difference is that here it is done sequentially while most methods sample one case each time. Not really meaningful or at least not proved meaningful here. * The sequential/RNN-like structure is unclear and it is hard to see how it relates to the data. * Writing that the proposed method "unsupervised, which is distinctly different from previous works" is not true or should be rephrased. The only difference comes from that the prediction function (softmax and not ranking for instance) and the loss used. But none of the methods compared in the experiments use more information than GEN (the original graph). GEN is not the only model using a softmax by the way. * The fact of predicting indistinctly a fact or its reverse seems rather worrying to me. Predicting that "John is_father_of Paul" or that "John is_child_of Paul" is not the same..! How is assessed the fact that a prediction is conceptually correct? Using types? * The bottom part of Table 2 is surprising. How come for the task of predicting Head, the model trained only at predicting heads (GEN(t,r => h)) performs worse than the model trained only at predicting tails (GEN(h,r => t))?