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