  Turn-level user satisfaction is one of the most important performance metrics
for conversational agents. It can be used to monitor the agent's performance
and provide insights about defective user experiences. Moreover, a powerful
satisfaction model can be used as an objective function that a conversational
agent continuously optimizes for. While end-to-end deep learning has shown
promising results, having access to a large number of reliable annotated
samples required by these methods remains challenging. In a large-scale
conversational system, there is a growing number of newly developed skills,
making the traditional data collection, annotation, and modeling process
impractical due to the required annotation costs as well as the turnaround
times. In this paper, we suggest a self-supervised contrastive learning
approach that leverages the pool of unlabeled data to learn user-agent
interactions. We show that the pre-trained models using the self-supervised
objective are transferable to the user satisfaction prediction. In addition, we
propose a novel few-shot transfer learning approach that ensures better
transferability for very small sample sizes. The suggested few-shot method does
not require any inner loop optimization process and is scalable to very large
datasets and complex models. Based on our experiments using real-world data
from a large-scale commercial system, the suggested approach is able to
significantly reduce the required number of annotations, while improving the
generalization on unseen out-of-domain skills.
