text: This paper proposes to bring together multiple inductive biases that hope to correct for inconsistencies in sequence decoding.  Building on previous works that utilize modified objectives to generate sequences, this work proposes to optimize for the parameters of a pre-defined combination of various sub-objectives.  The human evaluation is straight-forward and meaningful to compensate for the well-known inaccuracies of automatic evaluation.  While the paper points out that they introduce multiple inductive biases that are useful to produce human-like sentences,  it is not entirely correct that the objective is being learnt as claimed in portions of the paper.  I would like this point to be clarified better in the paper.  I think showing results on grounded generation tasks like machine translation or image-captioning would make a stronger case for evaluating relevance.  I would like to see comparisons on these tasks.