Speaker-sensitive dual memory networks for multi-turn slot tagging

Young-Bum Kim, Sungjin Lee, R. Sarikaya
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引用次数: 8

Abstract

In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information. To incorporate dialog history, we present a neural architecture with Speaker-Sensitive Dual Memory Networks which encode utterances differently depending on the speaker. This addresses the different extents of information available to the system — the system knows only the surface form of user utterances while it has the exact semantics of system output. We performed experiments on real user data from Microsoft Cortana, a commercial personal assistant. The result showed a significant performance improvement over the state-of-the-art slot tagging models using contextual information.
多轮槽标注的扬声器敏感双记忆网络
在多回合对话中,自然语言理解模型会由于忽略语境信息而导致明显的错误。为了结合对话历史,我们提出了一个具有说话人敏感双记忆网络的神经结构,该网络根据说话人的不同对话语进行不同的编码。这解决了系统可获得的不同程度的信息——系统只知道用户话语的表面形式,而它具有系统输出的确切语义。我们对微软商业个人助理Cortana的真实用户数据进行了实验。结果表明,与使用上下文信息的最先进的槽标记模型相比,性能有了显著提高。
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