text: This paper describes an attempt of improving information flow in deep networks (but is used and tested here with seq2seq models although it is reality unrelated to seq2seq models per se).  Slightly different from Resnet the information flow is improved by not just adding the outputs from previous layers but instead concatenating the outputs from previous layers with the current outputs.  The authors claim better convergence speed and better results for a similar number of parameters although the differences seems to be in the noise.   Overall this is an OK technique but in my opinion not really novel enough to justify a whole paper about it  as it seems more like a relatively minor architecture tweak.  The results seem to indicate that there were some problems with getting deeper networks to work for the baseline (why is in Table 3 baseline-6L worse than baseline-4L?) for which the reason could be a multitude of issues probably related to hyper-parameter tuning.  What is also missing is a an analysis of the negative consequences of this technique -- for example, doesn't the number of parameters increase with the depth of the network because of the concatenation?  Also, it would have been good to see more experiments with smaller baseline networks as well to match the smaller DenseNet networks in Table 1 and 2.  Finally, the writing of the paper could be improved a lot:  The basic idea is not well described (however, many times repeated)  and the grammar is often wrong  and also there are some typos.