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：尽管开放领域对话生成取得了巨大成功，但未见实体对对话生成任务具有显著影响，导致模型在对话生成中性能下降。先前研究利用已见实体的检索知识作为辅助数据来增强模型表示，然而，未见实体的逻辑解释（例如其可能的共现词、语义相似词及实体类别）仍有待探索。为此，我们提出了一种方法来解决上述挑战。通过提取文本中的实体节点构建图，利用实体的一跳邻域节点增强未见实体上下文的表示。此外，我们添加了命名实体标签预测任务，以应对图中不存在未见实体的问题。我们在开放数据集Wizard of Wikipedia上进行了实验，实证结果表明，我们的方法在Wizard of Wikipedia上优于当前最先进方法。