Complex Reading Comprehension Through Question Decomposition

Understand

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.