  Evaluation is crucial in the development process of task-oriented dialogue
systems. As an evaluation method, user simulation allows us to tackle issues
such as scalability and cost-efficiency, making it a viable choice for
large-scale automatic evaluation. To help build a human-like user simulator
that can measure the quality of a dialogue, we propose the following task:
simulating user satisfaction for the evaluation of task-oriented dialogue
systems. The purpose of the task is to increase the evaluation power of user
simulations and to make the simulation more human-like. To overcome a lack of
annotated data, we propose a user satisfaction annotation dataset, USS, that
includes 6,800 dialogues sampled from multiple domains, spanning real-world
e-commerce dialogues, task-oriented dialogues constructed through Wizard-of-Oz
experiments, and movie recommendation dialogues. All user utterances in those
dialogues, as well as the dialogues themselves, have been labeled based on a
5-level satisfaction scale. We also share three baseline methods for user
satisfaction prediction and action prediction tasks. Experiments conducted on
the USS dataset suggest that distributed representations outperform
feature-based methods. A model based on hierarchical GRUs achieves the best
performance in in-domain user satisfaction prediction, while a BERT-based model
has better cross-domain generalization ability.
