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.

翻译：上下文信息建模是对话式知识库问答中的重要任务。然而，现有方法通常假设话语独立性并对其进行孤立建模。本文提出一种历史语义图增强的知识库问答模型（HSGE），该模型能够有效建模对话历史中的长程语义依赖关系，同时保持较低的计算成本。该框架包含一个上下文感知编码器，采用动态记忆衰减机制并在不同粒度层级上对上下文进行建模。我们在广泛使用的复杂序列问答基准数据集上评估了HSGE，实验结果表明其在所有问题类型上的平均性能均优于现有基线方法。