Abstract
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.
Abstract (translated)
尽管开放域对话生成取得了巨大的成功,但 unseen entities 对对话生成任务具有很大的影响。这会导致对话生成模型的性能下降。以前的研究使用从已看到实体中提取的知识作为辅助数据来增强模型的表示。然而,未看到实体的逻辑解释仍然未被探索,例如它们和实体类别可能的共现或语义相似性词汇。在本文中,我们提出了一种方法来应对上述挑战。我们通过提取实体节点来构建一张图,增强未看到实体上下文的表示,同时加入命名实体标签预测任务,应用graph中未出现实体的问题。我们在维基百科开放数据集上进行了实验,实验结果表明,我们的方法在维基百科的“巫师”任务中比当前最先进的方法表现更好。
URL
https://arxiv.org/abs/2301.12850
