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.

中文翻译：

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用于多域对话状态跟踪的高效上下文和模式融合网络

对话状态跟踪 (DST) 旨在估计给定所有先前对话的当前对话状态。对于多域 DST，由于状态候选和对话长度的增加，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（预测的）和当前的对话话语作为 DST 的输入。为了考虑不同域槽之间的关系，利用了涉及先验知识的模式图。在本文中，提出了一种新的上下文和模式融合网络，通过使用内部和外部注意机制对对话上下文和模式图进行编码。实验结果表明，我们的方法可以在 MultiWOZ 2.0 和 MultiWOZ 2.1 基准测试中获得最先进的开放词汇 DST 性能。