知识感知的多类型对话推荐

Knowledge-aware Multi-type Conversational Recommendation

智能推荐型对话系统通过丰富的交互方式与用户进行交流,首先收集用户兴趣和偏好,然后主动地向用户推荐其感兴趣的内容。因此,该类系统通常涵盖多种对话类型,如问答、闲聊、推荐等。目前的研究采用流水线模型,存在误差累积的问题。该文提出基于Transformer的具有知识感知能力的对话生成模型完成面向推荐的多类型对话任务。该模型使用Transformer解码器隐式地学习对话目标路径并生成回复。此外,该文通过引入知识编码器和基于知识词表的Copy机制,提升模型对知识的感知能力。在DuRecDial数据集上的实验表明,提出的模型和基线模型相比在自动评估中取得了显著的性能提升,其中F1、BLEU与Distinct分别提升了59.08%、110%、66.14%。该模型在2020语言与智能技术竞赛:面向推荐的对话任务中获得第三名。

An intelligent recommended dialogue system communicates with users in rich interactive ways, which usually covers multiple types of dialogue, such as question answering, chit-chat, recommendation dialogs, etc. In contrast to the current pipeline model, we propose a knowledge-aware dialogue generation model based on Transformer to accomplish conversational recommendation over multi-type dialog task. We use a Transformer decoder to implicitly learn dialogue goal path and generate a reply. Additionally, we introduce a knowledge encoder and a copy mechanism to enhance the model’s ability to perceive knowledge. Experimental results on the DuRecDial dataset show that the proposed model achieves a significant improvement in terms of F1, BLEU and Distinct over the baseline models by 59.08%, 110.00%, 66.14%, respectively. Our model ranked at the third place in 2020 Language and Intelligent Technology Competition: Conversational Recommendation Task.