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            <namePart type="given">Lihu</namePart>
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    <abstract>Multi-hop question generation is a challenging task in natural language processing (NLP) that requires synthesizing information from multiple sources. We propose GNET-QG, a novel approach that integrates Graph Attention Networks (GAT) with sequence-to-sequence models, enabling structured reasoning over multiple information sources to generate complex questions. Our experiments demonstrate that GNET-QG outperforms previous state-of-the-art models across several evaluation metrics, particularly excelling in METEOR, showing its effectiveness in enhancing machine reasoning capabilities.</abstract>
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%0 Conference Proceedings
%T GNET-QG: Graph Network for Multi-hop Question Generation
%A Jamshidi, Samin
%A Chali, Yllias
%Y Gesese, Genet Asefa
%Y Sack, Harald
%Y Paulheim, Heiko
%Y Merono-Penuela, Albert
%Y Chen, Lihu
%S Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F jamshidi-chali-2025-gnet
%X Multi-hop question generation is a challenging task in natural language processing (NLP) that requires synthesizing information from multiple sources. We propose GNET-QG, a novel approach that integrates Graph Attention Networks (GAT) with sequence-to-sequence models, enabling structured reasoning over multiple information sources to generate complex questions. Our experiments demonstrate that GNET-QG outperforms previous state-of-the-art models across several evaluation metrics, particularly excelling in METEOR, showing its effectiveness in enhancing machine reasoning capabilities.
%U https://aclanthology.org/2025.genaik-1.3/
%P 20-26

Markdown (Informal)

[GNET-QG: Graph Network for Multi-hop Question Generation](https://aclanthology.org/2025.genaik-1.3/) (Jamshidi & Chali, GenAIK 2025)

• GNET-QG: Graph Network for Multi-hop Question Generation (Jamshidi & Chali, GenAIK 2025)

ACL

• Samin Jamshidi and Yllias Chali. 2025. GNET-QG: Graph Network for Multi-hop Question Generation. In Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK), pages 20–26, Abu Dhabi, UAE. International Committee on Computational Linguistics.