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arXiv · 2211.03277

Complex Reading Comprehension Through Question Decomposition

Xiao-Yu Guo·Yuan-Fang Li·Gholamreza Haffari
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Abstract

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.

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Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari. 2022-11-07. Complex Reading Comprehension Through Question Decomposition. https://arxiv.org/abs/2211.03277

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