Abstract
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.
Abstract (translated)
基于任务的对话系统通常需要进行交互级别的注释,例如对话状态和每个步骤系统采取的行动。这些注释可能会产生费用,具有错误率,并且需要领域和注释专业知识。随着LLM的进步,我们假设无标签数据和数据定义足以构建一个无需监督的 task-oriented 对话系统。仅使用(1)定义良好的 API 模式和(2)用户和代理之间的无标签对话,我们提出了一种通过噪声信道模型推断回合级别注释的新方法。我们通过期望最大化(EM)迭代改进这些伪标签,并使用推断的标签来训练端到端对话代理。在 MultiWOZ 基准上评估我们的方法,我们的方法将 strong GPT-3.5 基线的对话成功率加倍。
URL
https://arxiv.org/abs/2404.15219
