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.
@article{arxiv.2105.11174,
title = {Retrieval Enhanced Model for Commonsense Generation},
author = {Han Wang and Yang Liu and Chenguang Zhu and Linjun Shou and Ming Gong and Yichong Xu and Michael Zeng},
journal= {arXiv preprint arXiv:2105.11174},
year = {2021}
}