论文标题
模式引导对话状态跟踪的多任务BERT模型
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
论文作者
论文摘要
面向任务的对话系统通常使用对话状态跟踪器（DST）成功完成对话。最近的最新DST实现依赖于各种服务的模式来改善模型鲁棒性并处理对新领域的零击概括[1]，但是这种方法[2，3]通常需要多个大型变压器模型和长输入序列才能表现良好。我们提出了一个基于多任务BERT的单个模型，该模型共同求解了意图预测的三个DST任务，请求的插槽预测和插槽填充。此外，我们提出了对对话历史和服务模式的有效和简约编码，该编码旨在进一步提高性能。 SGD数据集的评估表明，我们的方法的表现优于基线SGP-DST，比最新的方法相比表现良好，而计算上的效率更高。进行了广泛的消融研究，以研究我们模型成功的促成因素。
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.
