Abstract
Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for
multi-hop RC that decomposes a compositional question into simpler sub-questions that
can be answered by off-the-shelf single-hop
RC models. Since annotations for such decomposition are expensive, we recast subquestion generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates
sub-questions that are as effective as humanauthored sub-questions. We also introduce a
new global rescoring approach that considers
each decomposition (i.e. the sub-questions and
their answers) to select the best final answer,
greatly improving overall performance. Our
experiments on HOTPOTQA show that this
approach achieves the state-of-the-art results,
while providing explainable evidence for its
decision making in the form of sub-questions