Latent Reasoning for Low-Resource Question Generation

Abstract

Multi-hop question generation requires complex reasoning and coherent language realization. Learning a generation model for the problem requires extensive multi-hop question answering (QA) data, which are limited due to the manual collection effort. A two-phase strategy addresses the insufficiency of multi-hop QA data by first generating and then composing single-hop sub-questions. Learning this generating and then composing two-phase model, however, requires manually labeled question decomposition data, which is labor intensive. To overcome this limitation, we propose a novel generative approach that optimizes the two-phase model without question decomposition data. We treat the unobserved ..