@inproceedings{sun-etal-2023-history,
    title = "History Semantic Graph Enhanced Conversational {KBQA} with Temporal Information Modeling",
    author = "Sun, Hao  and
      Li, Yang  and
      Deng, Liwei  and
      Li, Bowen  and
      Hui, Binyuan  and
      Li, Binhua  and
      Lan, Yunshi  and
      Zhang, Yan  and
      Li, Yongbin",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.195",
    doi = "10.18653/v1/2023.acl-long.195",
    pages = "3521--3533",
    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.",
}

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.
Sun, Hao Li, Yang Deng, Liwei Li, Bowen Hui, Binyuan Li, Binhua Lan, Yunshi Zhang, Yan Li, Yongbin
History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling