Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function

A. B. Siddique, M. H. Maqbool, Kshitija Taywade, Hassan Foroosh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


Task-oriented dialog systems enable users to accomplish tasks using natural language. State-of-the-art systems respond to users in the same way regardless of their personalities, although personalizing dialogues can lead to higher levels of adoption and better user experiences. Building personalized dialog systems is an important, yet challenging endeavor, and only a handful of works took on the challenge. Most existing works rely on supervised learning approaches and require laborious and expensive labeled training data for each user profile. Additionally, collecting and labeling data for each user profile is virtually impossible. In this work, we propose a novel framework, P-ToD, to personalize task-oriented dialog systems capable of adapting to a wide range of user profiles in an unsupervised fashion using a zero-shot generalizable reward function. P-ToD uses a pre-trained GPT-2 as a backbone model and works in three phases. Phase one performs task-specific training. Phase two kicks off unsupervised personalization by leveraging the proximal policy optimization algorithm that performs policy gradients guided by the zero-shot generalizable reward function. Our novel reward function can quantify the quality of the generated responses even for unseen profiles. The optional final phase fine-tunes the personalized model using a few labeled training examples. We conduct extensive experimental analysis using the personalized bAbI dialogue benchmark for five tasks and up to 180 diverse user profiles. The experimental results demonstrate that P-ToD, even when it had access to zero labeled examples, outperforms state-of-the-art supervised personalization models and achieves competitive performance on BLEU and ROUGE metrics when compared to a strong fully-supervised GPT-2 baseline.

Original languageEnglish
Title of host publicationCIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
Number of pages11
ISBN (Electronic)9781450392365
StatePublished - Oct 17 2022
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: Oct 17 2022Oct 21 2022

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2022 ACM.


  • dialog systems
  • personalization
  • reinforcement learning
  • zero-shot learning

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • General Decision Sciences


Dive into the research topics of 'Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function'. Together they form a unique fingerprint.

Cite this