End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion

Zonglin Yang, Xinya Du, Erik Cambria, Claire Cardie

Abstract:

Pretrained language models have been shown to store knowledge in their parameters and have achieved reasonable performance in commonsense knowledge base completion (CKBC) tasks. However, CKBC is knowledge-intensive and it is reported that pretrained language models’ performance in knowledge-intensive tasks are limited because of their incapability of accessing and manipulating knowledge. As a result, we hypothesize that providing retrieved passages that contain relevant knowledge as additional input to the CKBC
task will improve performance. In particular, we draw insights from Case-Based Reasoning (CBR) – which aims to solve a new problem by reasoning with retrieved relevant cases, and investigate the direct application of it to CKBC. On two benchmark datasets, we demonstrate through automatic and human evaluations that our End-to-end Case-Based Reasoning Framework (ECBRF) generates more valid knowledge than the state-of-the-art COMET model for CKBC in both the fully supervised and few-shot settings. From the perspective of
CBR, our framework addresses a fundamental question on whether CBR methodology can be utilized to improve deep learning models.