@inproceedings{zhang-yongmei-2024-enhancing-knowledge,
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing Knowledge Selection via Multi-level Document Semantic Graph
%A Zhang, Haoran
%A Yongmei, Tan
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhang-yongmei-2024-enhancing-knowledge
%X 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.
%U https://aclanthology.org/2024.lrec-main.531
%P 5996-6006
Markdown (Informal)
[Enhancing Knowledge Selection via Multi-level Document Semantic Graph](https://aclanthology.org/2024.lrec-main.531) (Zhang & Yongmei, LREC-COLING 2024)
ACL