Analogy-making gives rise to reasoning, abstraction, flexible categorization
and counterfactual inference -- abilities lacking in even the best AI systems
today. Much research has suggested that analogies are key to non-brittle
systems that can adapt to new domains. Despite their importance, analogies
received little attention in the NLP community, with most research focusing on
simple word analogies. Work that tackled more complex analogies relied heavily
on manually constructed, hard-to-scale input representations. In this work, we
explore a more realistic, challenging setup: our input is a pair of natural
language procedural texts, describing a situation or a process (e.g., how the
heart works/how a pump works). Our goal is to automatically extract entities
and their relations from the text and find a mapping between the different
domains based on relational similarity (e.g., blood is mapped to water). We
develop an interpretable, scalable algorithm and demonstrate that it identifies
the correct mappings 87% of the time for procedural texts and 94% for stories
from cognitive-psychology literature. We show it can extract analogies from a
large dataset of procedural texts, achieving 79% precision (analogy prevalence
in data: 3%). Lastly, we demonstrate that our algorithm is robust to
paraphrasing the input texts.

本研究通过探索一种更具有现实性且具有挑战性的方法，基于关系相似性来找到不同域之间的映射，使用自动提取文本中实体及其关系并进行解释性的可扩展性算法，从大量的自然语言过程文本数据集中提取类比，并证明该算法对于文本的释义具有鲁棒性。