  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
