Training task-oriented dialogue systems typically requires turn-level
annotations for interacting with their APIs: e.g. a dialogue state and the
system actions taken at each step. These annotations can be costly to produce,
error-prone, and require both domain and annotation expertise. With advances in
LLMs, we hypothesize unlabelled data and a schema definition are sufficient for
building a working task-oriented dialogue system, completely unsupervised.
Using only (1) a well-defined API schema (2) a set of unlabelled dialogues
between a user and agent, we develop a novel approach for inferring turn-level
annotations as latent variables using a noisy channel model. We iteratively
improve these pseudo-labels with expectation-maximization (EM), and use the
inferred labels to train an end-to-end dialogue agent. Evaluating our approach
on the MultiWOZ benchmark, our method more than doubles the dialogue success
rate of a strong GPT-3.5 baseline.

通过使用未标注的数据和模式定义，我们开发了一种新方法来构建一个完全无监督的面向任务的对话系统，该系统可以在迭代中通过期望最大化方法逐渐改进伪标签，并利用这些标签来训练一个端到端的对话代理，其在 MultiWOZ 基准测试上的成功率超过了强大的 GPT-3.5 基准的两倍。