@inproceedings{zhang-yongmei-2024-enhancing,
    title = "Enhancing Knowledge Selection via Multi-level Document Semantic Graph",
    author = "Zhang, Haoran  and
      Yongmei, Tan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.531",
    pages = "5996--6006",
    abstract = "Knowledge selection is a crucial sub-task of Document Grounded Dialogue System. Existing methods view knowledge selection as a sentence matching or classification. However, those methods can{'}t capture the semantic relationships within complex document. We propose a flexible method that can construct multi-level document semantic graph from the grounding document automatically and store semantic relationships within the documents effectively. Besides, we also devise an auxiliary task to leverage the graph more efficiently and can help the optimization of knowledge selection task. We conduct extensive experiments on public datasets: WoW(CITATION) and Holl-E(CITATION). And we achieves state-of-the-art result on WoW. Our code has been released at https://github.com/ddf62/multi-level-semantic-document-graph.",
}

Knowledge selection is a crucial sub-task of Document Grounded Dialogue System. Existing methods view knowledge selection as a sentence matching or classification. However, those methods can{'}t capture the semantic relationships within complex document. We propose a flexible method that can construct multi-level document semantic graph from the grounding document automatically and store semantic relationships within the documents effectively. Besides, we also devise an auxiliary task to leverage the graph more efficiently and can help the optimization of knowledge selection task. We conduct extensive experiments on public datasets: WoW(CITATION) and Holl-E(CITATION). And we achieves state-of-the-art result on WoW. Our code has been released at https://github.com/ddf62/multi-level-semantic-document-graph.
Zhang, Haoran Yongmei, Tan
Enhancing Knowledge Selection via Multi-level Document Semantic Graph