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Incorporating Specific Knowledge into End-to-End Task-oriented Dialogue Systems

机译：将特定知识融入端到端任务导向对话系统

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External knowledge is vital to many natural language processing tasks. However, current end-to-end dialogue systems often struggle to interface knowledge bases(KBs) with response smoothly and effectively. In this paper, we convert the raw knowledge into relation knowledge and integrated knowledge and then incorporate them into end-to-end task-oriented dialogue systems. The relation knowledge extracted from knowledge triples is combined with dialogue history, aiming to enhance semantic inputs and support better language understanding. Integrated knowledge involves entities and relations by graph attention, assisting the model in generating informative responses. The experimental results on three public dialogue datasets show that our model improves over the previous state-of-the-art models in sentence fluency and informativeness.
机译：外部知识对许多自然语言处理任务至关重要。然而，当前的端到端对话系统往往难以将知识库（KBs）与响应顺利有效地结合起来。在本文中，我们将原始知识转化为关系知识和集成知识，然后将它们整合到端到端的面向任务的对话系统中。从知识三元组中提取的关系知识与对话历史相结合，旨在增强语义输入，支持更好的语言理解。综合知识通过图形注意涉及实体和关系，帮助模型生成信息性响应。在三个公共对话数据集上的实验结果表明，我们的模型在句子流畅性和信息量方面比以前的先进模型有所提高。

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