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。