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Commonsense generation aims to generate a realistic sentence describing adaily scene under the given concepts, which is very challenging, since itrequires models to have relational reasoning and compositional generalizationcapabilities. Previous work focuses on retrieving prototype sentences for theprovided concepts to assist generation. They first use a sparse retriever toretrieve candidate sentences, then re-rank the candidates with a ranker.However, the candidates returned by their ranker may not be the most relevantsentences, since the ranker treats all candidates equally without consideringtheir relevance to the reference sentences of the given concepts. Anotherproblem is that re-ranking is very expensive, but only using retrievers willseriously degrade the performance of their generation models. To solve theseproblems, we propose the metric distillation rule to distill knowledge from themetric (e.g., BLEU) to the ranker. We further transfer the critical knowledgesummarized by the distilled ranker to the retriever. In this way, the relevancescores of candidate sentences predicted by the ranker and retriever will bemore consistent with their quality measured by the metric. Experimental resultson the CommonGen benchmark verify the effectiveness of our proposed method: (1)Our generation model with the distilled ranker achieves a new state-of-the-artresult. (2) Our generation model with the distilled retriever even surpassesthe previous SOTA.

Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning