Year: 2025
Type: conference-paper
Abstract: Inspired by theories in language psychology, it is natural to consider more constraints, such as intentions, logic, knowledge, etc., when a complex or multi-hop question is generated.As the subtask of Multi-Hop Question Generation (MHQG), the task of Multi-Constraint Multi-Hop Question Generation (MCHQG) is more aligned with human question theories.However, it is hard to determine how to bring various high-dimensional semantic constraints, and how to integrate each constraint across all hops when a multi-hop question is being generating.To address these challenges, we introduce an effective framework which includes constraint dimensionality reduction and divide-andconquer-based dynamic projection; we call it RUBY.The proposed RUBY contains a module of high-dimensional semantic constraint dimension reduction and a module of sub-question answer pairs-based multi-hop question generation.Meanwhile, a Reasoning Dynamic Projection strategy is tailored to effectively incorporate the constraints into every hop of the multi-hop question.The experimental results demonstrate that RUBY consistently outperforms baseline models, which suggest that RUBY is able to effectively capture and integrate semantic constraints, leading to more accurate and humanlike multi-hop question generation.We release the code and data to public 1 .
Authors: Wenzhuo Zhao, Shuangyin Li
Institution: South China Normal University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G8795516001, G8847136387
OpenAlex ID: W4412944889