E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning

TL;DR

E-KAR is a new benchmark designed to evaluate and explain the reasoning process of neural models in solving knowledge-intensive analogical questions, highlighting current models' limitations.

Contribution

This paper introduces E-KAR, the first benchmark with explainability for analogical reasoning, combining Chinese and English questions with manual annotations.

Findings

Current models struggle with explanation generation.

Models perform poorly on analogical question answering.

Benchmark reveals gaps in neural reasoning capabilities.

Abstract

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.