Research on Joint Modeling of Intent Detection and Slot Filling

Dan Yang, Chaoyang Geng, Yi Li
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Abstract

Abstract In task-based dialogue system, the key of the natural language understanding module is intent detection and slot filling. At this stage, Joint modeling of intention detection and slot filling tasks has become the mainstream and achieved good results. In order to investigate the correlation between intention detection and slot filling tasks, Joint model of intention detection and slot filling based on attention mechanism in three dimensions: one-way modeling from intention to slot, Unidirectional modeling from slot to intention and bidirectional modeling from intention to slot Separately. And experiments were conducted using the Chinese dataset CAIS, and the results showed three evaluation results for time slot F1.The intention accuracy and overall accuracy of joint models for intention detection and filling gaps are usually higher than those of unidirectional models.
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意图检测与缝隙填充联合建模研究
在基于任务的对话系统中,自然语言理解模块的关键是意图检测和插槽填充。现阶段,意图检测和槽填充任务的联合建模已成为主流,并取得了较好的效果。为了研究意向检测与槽位填充任务之间的相关性,基于注意机制,分别在意向到槽位的单向建模、槽位到意图的单向建模和意向到槽位的双向建模三个维度上建立了意向检测与槽位填充任务的联合模型。利用中国数据集CAIS进行了实验,得到了3个时隙F1的评价结果。用于意向检测和填补空白的联合模型的意向精度和总体精度通常高于单向模型。
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