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 generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.
We build a stronger natural language generator by discriminatively training scoring functions that rank candidate generations with respect to various qualities of good writing. This paper proposes to bring together multiple inductive biases that hope to correct for inconsistencies in sequence decoding and proposes to optimize for the parameters of a pre-defined combination of various sub-objectives.  This paper combines RNN language model with several discriminatively trained models to improve the language generation. This paper proposes to improve RNN language model generation using augmented objectives inspired by Grice's maxims of communication.