Ning Bo, Zhao Deji, Liu Xinyi, Li Guanyu
School of Information Science and Technology, Dalian Maritime University, Linghai road No.1, Dalian, 116026 Liaoning China.
World Wide Web. 2022 Sep 30:1-22. doi: 10.1007/s11280-022-01100-8.
Multi-turn dialogue generation is an essential and challenging subtask of text generation in the question answering system. Existing methods focused on extracting latent topic-level relevance or utilizing relevant external background knowledge. However, they are prone to ignore the fact that relying too much on latent aspects will lose subjective key information. Furthermore, there is not so much relevant external knowledge that can be used for referencing or a graph that has complete entity links. Dependency tree is a special structure that can be extracted from sentences, it covers the explicit key information of sentences. Therefore, in this paper, we proposed the EAGS model, which combines the subjective pivotal information from the explicit dependency tree with sentence implicit semantic information. The EAGS model is a knowledge graph enabled multi-turn dialogue generation model, and it doesn't need extra external knowledge, it can not only extract and build a dependency knowledge graph from existing sentences, but also prompt the node representation, which is shared with Bi-GRU each time step word embedding in node semantic level. We store the specific domain subgraphs built by the EAGS, which can be retrieved as external knowledge graph in the future multi-turn dialogue generation task. We design a multi-task training approach to enhance semantics and structure local feature extraction, and balance with the global features. Finally, we conduct experiments on Ubuntu large-scale English multi-turn dialogue community dataset and English Daily dialogue dataset. Experiment results show that our EAGS model performs well on both automatic evaluation and human evaluation compared with the existing baseline models.
多轮对话生成是问答系统中文本生成的一个重要且具有挑战性的子任务。现有方法主要集中在提取潜在的主题级相关性或利用相关的外部背景知识。然而，它们容易忽略一个事实，即过度依赖潜在方面会丢失主观关键信息。此外，没有那么多相关的外部知识可用于参考，也没有一个具有完整实体链接的图。依存树是一种可以从句子中提取的特殊结构，它涵盖了句子的显式关键信息。因此，在本文中，我们提出了EAGS模型，该模型将来自显式依存树的主观关键信息与句子隐含语义信息相结合。EAGS模型是一个基于知识图谱的多轮对话生成模型，它不需要额外的外部知识，不仅可以从现有句子中提取并构建依存知识图谱，还能在节点语义层面提示节点表示，该表示与双向门控循环单元（Bi-GRU）每次时间步的词嵌入共享。我们存储由EAGS构建的特定领域子图，在未来的多轮对话生成任务中可将其作为外部知识图谱进行检索。我们设计了一种多任务训练方法来增强语义和结构局部特征提取，并与全局特征保持平衡。最后，我们在Ubuntu大规模英语多轮对话社区数据集和英语日常对话数据集上进行了实验。实验结果表明，与现有的基线模型相比，我们的EAGS模型在自动评估和人工评估中均表现良好。
