Retrieval Enhanced Model for Commonsense Generation - Wang, Han; Liu, Yang; Zhu, Chenguang; Shou (寿林钧), Linjun; Gong (YIMING), Ming; Xu, Yichong; Zeng, Michael (ACL-IJCNLP 2021, 2021)
Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts. Its requirement of reasoning over commonsense knowledge and compositional generalization ability even puzzles strong pre-trained language generation models. We propose a novel framework using retrieval methods to enhance both the pre-training and fine-tuning for commonsense generation. We retrieve prototype sentence candidates by concept matching and use them as auxiliary input. For fine-tuning, we further boost its performance with a trainable sentence retriever. We demonstrate experimentally on the large-scale CommonGen benchmark that our approach achieves new state-of-the-art results.