论文标题
多域对话状态跟踪的有效上下文和模式融合网络
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
论文作者
论文摘要
对话状态跟踪（DST）旨在估计所有上述对话的当前对话状态。对于多域DST，由于州候选人的数量和对话长度的增加，数据稀疏问题是一个主要障碍。为了有效地编码对话上下文，我们利用先前的对话状态（预测）和当前的对话话语作为DST的输入。要考虑不同域插槽之间的关系，利用了涉及先验知识的模式图。在本文中，提出了一个新颖的环境和模式融合网络，以使用内部和外部注意机制来编码对话上下文和模式图。实验结果表明，我们的方法可以在Multiwoz 2.0和Multiwoz 2.1基准测试上获得开放式DST的新最先进的性能。
Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can obtain new state-of-the-art performance of the open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.
