@inproceedings{yu2022retrieval,
author = {Yu, Wenhao and Zhu, Chenguang and Zhang, Zhihan and Wang, Shuohang and Zhang, Zhuosheng and Fang, Yuwei and Jiang, Meng},
title = {Retrieval Augmentation for Commonsense Reasoning: A Unified Approach},
booktitle = {Empirical Methods in Natural Language Processing (EMNLP) 2022},
year = {2022},
month = {December},
abstract = {A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and relation spaces that can be modeled. However, applying such methods to commonsense reasoning tasks faces two unique challenges, i.e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever. In this paper, we systematically investigate how to leverage commonsense knowledge retrieval to improve commonsense reasoning tasks. We proposed a unified framework of Retrieval-Augmented Commonsense reasoning (called RACo), including a newly constructed commonsense corpus with over 20 million documents and novel strategies for training a commonsense retriever. We conducted experiments on four different commonsense reasoning tasks. Extensive evaluation results showed that our proposed RACo can significantly outperform other knowledge-enhanced method counterparts, achieving new SoTA performance on the CommonGen and CREAK leaderboards.},
url = {http://approjects.co.za/?big=en-us/research/publication/retrieval-augmentation-for-commonsense-reasoning-a-unified-approach/},
}