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.
@article{arxiv.2211.03277,
title = {Complex Reading Comprehension Through Question Decomposition},
author = {Xiao-Yu Guo and Yuan-Fang Li and Gholamreza Haffari},
journal= {arXiv preprint arXiv:2211.03277},
year = {2022}
}