  Open-ended Commonsense Reasoning is defined as solving a commonsense question
without providing 1) a short list of answer candidates and 2) a pre-defined
answer scope. Conventional ways of formulating the commonsense question into a
question-answering form or utilizing external knowledge to learn
retrieval-based methods are less applicable in the open-ended setting due to an
inherent challenge. Without pre-defining an answer scope or a few candidates,
open-ended commonsense reasoning entails predicting answers by searching over
an extremely large searching space. Moreover, most questions require implicit
multi-hop reasoning, which presents even more challenges to our problem. In
this work, we leverage pre-trained language models to iteratively retrieve
reasoning paths on the external knowledge base, which does not require
task-specific supervision. The reasoning paths can help to identify the most
precise answer to the commonsense question. We conduct experiments on two
commonsense benchmark datasets. Compared to other approaches, our proposed
method achieves better performance both quantitatively and qualitatively.
