  An important unexplored aspect in previous work on user satisfaction
estimation for Task-Oriented Dialogue (TOD) systems is their evaluation in
terms of robustness for the identification of user dissatisfaction: current
benchmarks for user satisfaction estimation in TOD systems are highly skewed
towards dialogues for which the user is satisfied. The effect of having a more
balanced set of satisfaction labels on performance is unknown. However,
balancing the data with more dissatisfactory dialogue samples requires further
data collection and human annotation, which is costly and time-consuming. In
this work, we leverage large language models (LLMs) and unlock their ability to
generate satisfaction-aware counterfactual dialogues to augment the set of
original dialogues of a test collection. We gather human annotations to ensure
the reliability of the generated samples. We evaluate two open-source LLMs as
user satisfaction estimators on our augmented collection against
state-of-the-art fine-tuned models. Our experiments show that when used as
few-shot user satisfaction estimators, open-source LLMs show higher robustness
to the increase in the number of dissatisfaction labels in the test collection
than the fine-tuned state-of-the-art models. Our results shed light on the need
for data augmentation approaches for user satisfaction estimation in TOD
systems. We release our aligned counterfactual dialogues, which are curated by
human annotation, to facilitate further research on this topic.
