  Contrastive learning has achieved impressive success in generation tasks to
militate the "exposure bias" problem and discriminatively exploit the different
quality of references. Existing works mostly focus on contrastive learning on
the instance-level without discriminating the contribution of each word, while
keywords are the gist of the text and dominant the constrained mapping
relationships. Hence, in this work, we propose a hierarchical contrastive
learning mechanism, which can unify hybrid granularities semantic meaning in
the input text. Concretely, we first propose a keyword graph via contrastive
correlations of positive-negative pairs to iteratively polish the keyword
representations. Then, we construct intra-contrasts within instance-level and
keyword-level, where we assume words are sampled nodes from a sentence
distribution. Finally, to bridge the gap between independent contrast levels
and tackle the common contrast vanishing problem, we propose an inter-contrast
mechanism that measures the discrepancy between contrastive keyword nodes
respectively to the instance distribution. Experiments demonstrate that our
model outperforms competitive baselines on paraphrasing, dialogue generation,
and storytelling tasks.
