Multi-Hop Question Generation via Dual-Perspective Keyword Guidance (2025)

Abstract: Multi-hop question generation (MQG) aims to generate questions that require synthesizing multiple information snippets from documents to derive target answers. The primary challenge lies in effectively pinpointing crucial information snippets related to question-answer (QA) pairs, typically relying on keywords. However, existing works fail to fully utilize the guiding potential of keywords and neglect to differentiate the distinct roles of question-specific and document-specific keywords. To address this, we define dual-perspective keywords—question and document keywords—and propose a Dual-Perspective Keyword-Guided (DPKG) framework, which seamlessly integrates keywords into the multi-hop question generation process. We argue that question keywords capture the questioner’s intent, whereas document keywords reflect the content related to the QA pair. Functionally, question and document keywords work together to pinpoint essential information snippets in the document, with question keywords required to appear in the generated question. The DPKG framework consists of an expanded transformer encoder and two answer-aware transformer decoders for keyword and question generation, respectively. Extensive experiments on HotpotQA demonstrate the effectiveness of our work, showcasing its promising performance and underscoring its significant value in the MQG task.

## introduction question generation (qg) is the task of generating questions based on given documents and target answers @xcite . it is a vital area in natural language processing (nlp), with considerable potential for applications in automated question answering, reading comprehension, and related fields @xcite 2024). compared to the qg task, multi-hop question generation (mqg) aims to generate questions that require synthesizing multiple information snippets from documents to derive target answers, making it more representative of real-world scenarios @xcite @xcite . the primary challenge in mqg is effectively pinpointing crucial information snippets related to question-answer (qa) pairs in documents, a task that many studies have approached using keywords @xcite @xcite . however, current research does not fully leverage the guiding potential of keywords. some studies focus exclusively on document-specific keywords @xcite , while others, including more recent, higher-performing models @xcite consider constraints on question-specific keywords during question generation. according to our un-derstanding, as illustrated in figure 1 (a), question keywords reflect the questioner's intent, whereas document keywords reflect the content related to the qa pair in documents. functionally, question and document keywords work together to pinpoint essential information snippets in the document, with question keywords required to appear in the generated question. however, existing studies overlook the distinction between the two keyword types, which poses a challenge to further improving performance @xcite @xcite . to break through the above limitation, we define dual-perspective keywords: question keywords, which capture the intent of the questioner, and document keywords, which reflect the content related to the qa pair in documents (figure 1(a) ). based on this, we further propose a dual-perspective keyword-guided (dpkg) framework, which seamlessly integrates keywords into the multi-hop question generation process (figure 1(b) ). the framework clarifies essential information snippets by differentiating the two categories of keywords and ensures that specific keywords appear in the generated question, aligning closely with natural human intuition. more specifically, the dpkg framework consists of three main components: (1) an expanded transformer encoder that processes the document and target answer; (2) a decoder that generates dual-perspective keywords; and (3) another decoder that generates multi-hop questions by leveraging crucial information snippets pinpointed by keywords. this framework tackles mqg challenges and aligns with practical requirements. our contributions are as follows: ## related work question generation (qg), also known as shallow qg, involves generating questions based on given documents and target answers. early approaches to qg were rule-based @xcite @xcite , but their performance was limited. as the field advanced, attention-based sequence-to-sequence architectures gained popularity @xcite @xcite . some studies emphasized feature-rich encoders to capture more contextual information @xcite ), and answer-aware features were introduced to extract additional information @xcite . to address the challenge posed by long passages, a maxout pointer mechanism was developed @xcite . moreover, reinforcement learning (rl) has been applied in a graph-to-sequence model (g2s) for question generation @xcite . all of these studies focus on shallow qg, with the squad dataset @xcite being the most commonly used benchmark. multi-hop question generation (mqg) focuses on generating questions that synthesize multiple information snippets from documents to derive target answers. compared to shallow qg, mqg is more demanding and has garnered significant attention in recent years @xcite @xcite @xcite . in the early stages of mqg, graph neural networks (gnns) were integrated into multi-hop reasoning steps to facilitate question generation @xcite . however, with the evolution of transformer-based architectures, transformers have been demonstrated to outperform gnns in this task @xcite . to further enhance the performance of pre-trained language models (plms) in multi-hop question generation, researchers have incorporated mechanisms such as additional question-answer modules and multi-level content planning @xcite . numerous studies have also explored auxiliary tasks, proposing methods like order-agnostic learning and sequential rewriting to improve question quality @xcite @xcite . more recently, question-specific keywords have been utilized during decoding to enhance high-quality generation @xcite supported multi-hop question generation by identi-fying salient, related keywords or sentences. however, they do not fully harness the guiding potential of keywords, which limits improvements in generation performance. ## conclusion in this paper, we define dual-perspective keywords: question keywords, which capture the questioner's intent, and document keywords, which reflect the content related to qa pairs in documents. furthermore, we propose a dpkg framework that seamlessly integrates keywords into the multi-hop question generation process. we analyze the role of different keywords in guiding question generation and demonstrate that dual-perspective keywords are effective, especially in hard mode, providing more powerful guidance. extensive experiments demonstrate the effectiveness of our work, showcasing its promising performance and underscoring its significant value in the mqg task.