@inproceedings{wang2021retrieval,
author = {Wang, Han and Liu, Yang and Zhu, Chenguang and Shou (寿林钧), Linjun and Gong (YIMING), Ming and Xu, Yichong and Zeng, Michael},
title = {Retrieval Enhanced Model for Commonsense Generation},
booktitle = {ACL-IJCNLP 2021},
year = {2021},
month = {August},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/retrieval-enhanced-model-for-commonsense-generation/},
}