Ziquan Fu


2022

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E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning
Jiangjie Chen | Rui Xu | Ziquan Fu | Wei Shi | Zhongqiao Li | Xinbo Zhang | Changzhi Sun | Lei Li | Yanghua Xiao | Hao Zhou
Findings of the Association for Computational Linguistics: ACL 2022

The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve. More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer. Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area.