Paper:
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language. Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory. We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice's Maxims. In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation. Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation. Human evaluation demonstrates that text generated by the resulting system is preferred over that of baselines by a large margin and significantly enhances the overall coherence, style, and information content of the generated text.

Review:
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. 
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After reading the paper in detail again and the replies, I am downgrading my rating for this paper. While I really like the motivation and the evaluation proposed by this work, I believe that fixing the mismatch between the goals and the actual approach will make for a stronger work. 
As pointed out by other reviewers, while the goals and evaluation seem to be more aligned with Gricean maxims, some components of the objective are confusing. For instance, the length penalty encourages longer sentences violating quantity, manner (be brief) and potentially relevance. Further, the repetition model address the issue of RNNs failing to capture long-term contextual dependencies -- how much does such a modified objective affect models with attention / hierarchical models is not clear from the formulation. 
As pointed out in my initial review evaluation of relevance on the current task is not entirely convincing. A very wide variety of topics are feasible for a given context sentence. Grounded generation like MT / captioning would have been a more convincing evaluation. For example, Wu et al. (and other MT works) use a coverage term and this might be one of the indicators of relevance. 
Finally, I am not entirely convinced by the update regarding "learning the objective". While I agree with the authors that the objective function is being dynamically updated, the qualities of good language is encoded manually using a wide variety of additional objectives and only the relative importance of each of them is learnt.