  Multi-relation Question Answering is a challenging task, due to the
requirement of elaborated analysis on questions and reasoning over multiple
fact triples in knowledge base. In this paper, we present a novel model called
Interpretable Reasoning Network that employs an interpretable, hop-by-hop
reasoning process for question answering. The model dynamically decides which
part of an input question should be analyzed at each hop; predicts a relation
that corresponds to the current parsed results; utilizes the predicted relation
to update the question representation and the state of the reasoning process;
and then drives the next-hop reasoning. Experiments show that our model yields
state-of-the-art results on two datasets. More interestingly, the model can
offer traceable and observable intermediate predictions for reasoning analysis
and failure diagnosis, thereby allowing manual manipulation in predicting the
final answer.
