Task: Language Identification
Input (Spanish, English): The paper presents a method to learn graph embeddings in a unsupervised way using random walks. It is well written and the execution appears quite accurate. The area of learning whole graph representations does not seem to be very well explored in general, and the proposed approach enjoys having very few competitors.  In a nutshell, the idea is to linearize the graph using random walks and to compute the embedding of the central segment of each walk using the skip-thought criterion. Being not an expert in biology, I can not comment whether or not this makes sense, but the gains reported in Table 2 are quite significant.   An anonymous public comment compared this work to a number of others in which the problem of learning representations of nodes is considered. While this is arguably a different goal, one natural baseline would be to pool these representations using mean- or max- pooling. It would very interesting to do such a comparison, especially given that the considered approach heavily relies on pooling (see Figure 3(c))  To sum up, I think it is a nice paper, and with more baselines I would be ready to further increase the numerical score.
Output (English): en