@inproceedings{c4041b2a2d7a472c8be7ae4a6250ea3f,
title = "History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling",
abstract = "Context information modeling is an important task in conversational KBQA. However, existing methods usually assume the independence of utterances and model them in isolation. In this paper, we propose a History Semantic Graph Enhanced KBQA model (HSGE) that is able to effectively model long-range semantic dependencies in conversation history while maintaining low computational cost. The framework incorporates a context-aware encoder, which employs a dynamic memory decay mechanism and models context at different levels of granularity. We evaluate HSGE on a widely used benchmark dataset for complex sequential question answering. Experimental results demonstrate that it outperforms existing baselines averaged on all question types.",
author = "Hao Sun and Yang Li and Liwei Deng and Bowen Li and Binyuan Hui and Binhua Li and Yunshi Lan and Yan Zhang and Yongbin Li",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
doi = "10.18653/v1/2023.acl-long.195",
language = "英语",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "3521--3533",
booktitle = "Long Papers",
address = "澳大利亚",
}