Although the great success of open-domain dialogue generation, unseen entities can have a large impact on the dialogue generation task. It leads to performance degradation of the model in the dialog generation. Previous researches used retrieved knowledge of seen entities as the auxiliary data to enhance the representation of the model. Nevertheless, logical explanation of unseen entities remains unexplored, such as possible co-occurrence or semantically similar words of them and their entity category. In this work, we propose an approach to address the challenge above. We construct a graph by extracting entity nodes in them, enhancing the representation of the context of the unseen entity with the entity's 1-hop surrounding nodes. Furthermore, We added the named entity tag prediction task to apply the problem that the unseen entity does not exist in the graph. We conduct our experiments on an open dataset Wizard of Wikipedia and the empirical results indicate that our approach outperforms the state-of-the-art approaches on Wizard of Wikipedia.

翻译：尽管开域对话的产生取得了巨大成功,但隐形实体可以对对话生成任务产生巨大影响,这会导致该模式在对话生成过程中的性能退化。以前的研究利用了作为模型代表的辅助数据的被确认实体的知识,但对隐形实体的逻辑解释仍未得到探讨,例如它们及其实体类别的可能共生或语义相似的词句。在这项工作中,我们提出了应对上述挑战的方法。我们通过在其中提取实体节点来绘制一个图表,用该实体的1个节点周围节点来强化隐形实体的背景。此外,我们添加了被命名实体的标记预测任务,以应用图中不存在的隐形实体的问题。我们在维基百科的开放数据集向导上进行了实验,实验结果显示,我们的方法超越了维基百科精华中最先进的方法。