Complex Reading Comprehension Through Question Decomposition

• URL: http://arxiv.org/abs/2211.03277v1
• Date: Mon, 7 Nov 2022 02:54:04 GMT
• Title: Complex Reading Comprehension Through Question Decomposition
• Authors: Xiao-Yu Guo, Yuan-Fang Li, and Gholamreza Haffari
• Abstract summary: We propose a novel learning approach that helps language models better understand difficult multi-hop questions. Our model first learns to decompose each multi-hop question into several sub-questions by a trainable question decomposer. We leverage a reading comprehension model to predict the answer in a sequence-to-sequence manner.
• Score: 48.256818683923626
• License: http://creativecommons.org/licenses/by/4.0/
• 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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