  Generative commonsense reasoning requires machines to generate sentences
describing an everyday scenario given several concepts, which has attracted
much attention recently. However, existing models cannot perform as well as
humans, since sentences they produce are often implausible and grammatically
incorrect. In this paper, inspired by the process of humans creating sentences,
we propose a novel Knowledge-enhanced Commonsense Generation framework, termed
KGR^4, consisting of four stages: Retrieval, Retrospect, Refine, Rethink. Under
this framework, we first perform retrieval to search for relevant sentences
from external corpus as the prototypes. Then, we train the generator that
either edits or copies these prototypes to generate candidate sentences, of
which potential errors will be fixed by an autoencoder-based refiner. Finally,
we select the output sentence from candidate sentences produced by generators
with different hyper-parameters. Experimental results and in-depth analysis on
the CommonGen benchmark strongly demonstrate the effectiveness of our
framework. Particularly, KGR^4 obtains 33.56 SPICE points in the official
leaderboard, outperforming the previously-reported best result by 2.49 SPICE
points and achieving state-of-the-art performance.

Computation and Language