Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals. However, such systems rely on costly manually labeled dialogs which are not available in practical scenarios. In this paper, we present our models for Track 2 of the SereTOD 2022 challenge, which is the first challenge of building semi-supervised and reinforced TOD systems on a large-scale real-world Chinese TOD dataset MobileCS. We build a knowledge-grounded dialog model to formulate dialog history and local KB as input and predict the system response. And we perform semi-supervised pre-training both on the labeled and unlabeled data. Our system achieves the first place both in the automatic evaluation and human interaction, especially with higher BLEU (+7.64) and Success (+13.6\%) than the second place.

本文介绍了我们的Track 2模型，利用大规模真实世界的MobileCS中国TOD数据集，使用半监督学习和强化学习构建知识引导的对话模型，实现了任务导向对话系统的高效预测和人机交互。我们的系统在自动评估和人工交互中表现都极佳，尤其是 BLEU (+7.64) 和 Success (+13.6%) 方面比第二名更优秀。

面向任务导向对话系统的半监督知识驱动预训练