  Commonsense generation aims to generate a realistic sentence describing a
daily scene under the given concepts, which is very challenging, since it
requires models to have relational reasoning and compositional generalization
capabilities. Previous work focuses on retrieving prototype sentences for the
provided concepts to assist generation. They first use a sparse retriever to
retrieve candidate sentences, then re-rank the candidates with a ranker.
However, the candidates returned by their ranker may not be the most relevant
sentences, since the ranker treats all candidates equally without considering
their relevance to the reference sentences of the given concepts. Another
problem is that re-ranking is very expensive, but only using retrievers will
seriously degrade the performance of their generation models. To solve these
problems, we propose the metric distillation rule to distill knowledge from the
metric (e.g., BLEU) to the ranker. We further transfer the critical knowledge
summarized by the distilled ranker to the retriever. In this way, the relevance
scores of candidate sentences predicted by the ranker and retriever will be
more consistent with their quality measured by the metric. Experimental results
on the CommonGen benchmark verify the effectiveness of our proposed method: (1)
Our generation model with the distilled ranker achieves a new state-of-the-art
result. (2) Our generation model with the distilled retriever even surpasses
the previous SOTA.
