  Multi-hop reading comprehension requires not only the ability to reason over
raw text but also the ability to combine multiple evidence. We propose a novel
learning approach that helps language models better understand difficult
multi-hop questions and perform "complex, compositional" reasoning. Our model
first learns to decompose each multi-hop question into several sub-questions by
a trainable question decomposer. Instead of answering these sub-questions, we
directly concatenate them with the original question and context, and leverage
a reading comprehension model to predict the answer in a sequence-to-sequence
manner. By using the same language model for these two components, our best
seperate/unified t5-base variants outperform the baseline by 7.2/6.1 absolute
F1 points on a hard subset of DROP dataset.
