Learning Personalized End-to-End Goal-Oriented Dialog


  • Liangchen Luo Peking University
  • Wenhao Huang Shanghai Discovering Investment
  • Qi Zeng Peking University
  • Zaiqing Nie Alibaba
  • Xu Sun Peking University




Most 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 MEMN2N. 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.




How to Cite

Luo, L., Huang, W., Zeng, Q., Nie, Z., & Sun, X. (2019). Learning Personalized End-to-End Goal-Oriented Dialog. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6794-6801. https://doi.org/10.1609/aaai.v33i01.33016794



AAAI Technical Track: Natural Language Processing