  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.
