Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations. Recent state-of-the-art DST implementations rely on schemata of diverse services to improve model robustness and handle zero-shot generalization to new domains [1], however such methods [2, 3] typically require multiple large scale transformer models and long input sequences to perform well. We propose a single multi-task BERT-based model that jointly solves the three DST tasks of intent prediction, requested slot prediction and slot filling. Moreover, we propose an efficient and parsimonious encoding of the dialogue history and service schemata that is shown to further improve performance. Evaluation on the SGD dataset shows that our approach outperforms the baseline SGP-DST by a large margin and performs well compared to the state-of-the-art, while being significantly more computationally efficient. Extensive ablation studies are performed to examine the contributing factors to the success of our model.

文章提出了一种基于多任务BERT模型的对话状态跟踪器，可以同时解决意图预测、请求插槽预测和插槽填充等三个DST任务，并采用高效和简洁的对话历史和服务架构编码来提高性能。通过对SGD数据集进行的评估，结果表明该方法在计算效率明显提高的同时，大幅度超越了基线SGP-DST，并且性能表现接近于最先进的模型。

基于Multi-Task BERT模型的Schema-Guided对话状态跟踪