  Inductive reasoning is a core component of human intelligence. In the past
research of inductive reasoning within computer science, formal language is
used as representations of knowledge (facts and rules, more specifically).
However, formal language can cause systematic problems for inductive reasoning
such as disability of handling raw input such as natural language,
sensitiveness to mislabeled data, and incapacity to handle ambiguous input. To
this end, we propose a new paradigm (task) for inductive reasoning, which is to
induce natural language rules from natural language facts, and create a dataset
termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts
are written in natural language. New automatic metrics are also proposed and
analysed for the evaluation of this task. With DEER, we investigate a modern
approach for inductive reasoning where we use natural language as
representation for knowledge instead of formal language and use pretrained
language models as ''reasoners''. Moreover, we provide the first and
comprehensive analysis of how well pretrained language models can induce
natural language rules from natural language facts. We also propose a new
framework drawing insights from philosophy literature for this task, which we
show in the experiment section that surpasses baselines in both automatic and
human evaluations. We discuss about our future perspectives for inductive
reasoning in Section 7. Dataset and code are available at
https://github.com/ZonglinY/Inductive_Reasoning.
