@inproceedings{jamshidi-chali-2025-gnet,
    title = "{GNET}-{QG}: Graph Network for Multi-hop Question Generation",
    author = "Jamshidi, Samin  and
      Chali, Yllias",
    editor = "Gesese, Genet Asefa  and
      Sack, Harald  and
      Paulheim, Heiko  and
      Merono-Penuela, Albert  and
      Chen, Lihu",
    booktitle = "Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.genaik-1.3/",
    pages = "20--26",
    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."
}