TitleLearning Personalized End-to-End Goal-Oriented Dialog
AuthorsLuo, Liangchen
Huang, Wenhao
Zeng, Qi
Nie, Zaiqing
Sun, Xu
AffiliationPeking Univ, Sch EECS, MOE Key Lab Computat Linguist, Beijing, Peoples R China
Shanghai Discovering Investment, Shanghai, Peoples R China
Alibaba AI Labs, Beijing, Peoples R China
Microsoft Res Asia, Beijing, Peoples R China
Issue Date2019
PublisherTHIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
AbstractMost existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEN4N2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.
URIhttp://hdl.handle.net/20.500.11897/544303
IndexedCPCI-S(ISTP)
Appears in Collections:信息科学技术学院
计算语言学教育部重点实验室

Files in This Work
There are no files associated with this item.

Web of Science®



Checked on Last Week

百度学术™



Checked on Current Time




License: See PKU IR operational policies.