  Multi-hop reasoning has been widely studied in recent years to obtain more
interpretable link prediction. However, we find in experiments that many paths
given by these models are actually unreasonable, while little works have been
done on interpretability evaluation for them. In this paper, we propose a
unified framework to quantitatively evaluate the interpretability of multi-hop
reasoning models so as to advance their development. In specific, we define
three metrics including path recall, local interpretability, and global
interpretability for evaluation, and design an approximate strategy to
calculate them using the interpretability scores of rules. Furthermore, we
manually annotate all possible rules and establish a Benchmark to detect the
Interpretability of Multi-hop Reasoning (BIMR). In experiments, we run nine
baselines on our benchmark. The experimental results show that the
interpretability of current multi-hop reasoning models is less satisfactory and
is still far from the upper bound given by our benchmark. Moreover, the
rule-based models outperform the multi-hop reasoning models in terms of
performance and interpretability, which points to a direction for future
research, i.e., we should investigate how to better incorporate rule
information into the multi-hop reasoning model. Our codes and datasets can be
obtained from https://github.com/THU-KEG/BIMR.
