A memory network based end-to-end personalized task-oriented dialogue generation
作者:
Highlights:
•
摘要
Building a personalized task-oriented dialogue system is an important but challenging task. Significant success has been achieved in the template selection responses. However, preparing a massive response template is time-consuming and human-labor intensive. In this paper, we propose an end-to-end framework based on memory networks for response generation in a personalized task-oriented dialogue system. Our model consists of three parts: a retrieval module, a memory encoder network and a memory decoder network. Retrieval module employs the user utterances and user attributes to collect relevant responses from other users. Memory encoder is trained with textual features to obtain dialogue representation. Memory decoder is composed of an RNN and a rule-memory network for response generation. Experiments on the benchmark dataset show that our model achieves better performance than strong baselines in personalized task-oriented dialogue generation.
论文关键词:Task-oriented dialogue system,Dialogue generation,Personalized response
论文评审过程:Received 31 December 2019, Revised 2 August 2020, Accepted 8 August 2020, Available online 18 August 2020, Version of Record 28 August 2020.
论文官网地址:https://doi.org/10.1016/j.knosys.2020.106398