End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion
Zonglin Yang, Xinya Du, Erik Cambria, Claire Cardie · 2023
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.