Q2SM: 基于BERT的多领域任务型对话系统状态跟踪算法

基于管道的方法是目前任务型对话系统的主要构建方式,在工业界具有广泛应用,而对话状态跟踪(dialogue state tracking,DST)是任务型对话系统中的核心任务。面对传统的方法在多领域场景下表现较差的问题,该文结合语言模型预训练的最新研究成果,该文提出了一种基于BERT的对话状态跟踪算法Q2SM(query to state model)。该模型的上游使用了基于BERT的句子表征与相似度交互的槽判定模块,下游使用了一种面向对话状态跟踪任务的自定义RNN: DST-RNN。在WOZ 2.0和MultiWOZ 2.0两个数据集上的实验表明,Q2SM相比于之前的最好模型,分别在联合准确率和状态F₁值两个评价指标上提升了1.09%和2.38%。此外,模型消融实验验证了,DST-RNN相比于传统的RNN或LSTM,不仅可以提升评价指标值,还可以加快模型的收敛速度。

Q2SM: Dialogue State Tracking Algorithm Based on BERT for Multi-Domain Task-Oriented Dialogue System

Dialogue State Tracking(DST) is the core task in task-oriented dialogue system. For multi-domain task dialogue system, this paper introduces a novel model named Q2SM(Query to State Model) based on BERT. The upstream of Q2SM is a model based on sentence representation with BERT and similarity, while the downstream is a new type of RNN named DST-RNN. Experiments on WOZ2.0 and MultiWOZ2.0 datasets show that the model outperforms the existing state-of-the-art model by 1.09% in joint-accuracy and 2.38% in F₁ score. And the model ablation study show that the DST-RNN could also speed up model convergence.