Abstract: In the natural language understanding module,there are two tasks of intent detection and slot filling,and there is a strong correlation between the two tasks that the generation of slot information is highly dependent on the intent information.However,most of the existing works regard it as two independent tasks to achieve,resulting in that the accuracy of the dialogue system cannot be further improved.To this end,aiming at the correlation information between the intent detection task and the slot filling task in the dialogue system,on the basis of the existing work,an end-to-end network model based on the idea of  Stack-Propagation was proposed.The idea of the Stack-Propagation framework in the decoder stage was borrowed and improved,which added the result of intent detection to the input of the slot filling task,and used the result of the intent detection to further guide the slot filling task.Through experiments on SMD dataset,it was proved that the model could not only make full use of the correlation information between the intent detection task and the slot filling task,but also achieve the effect of mutual promotion through joint learning,and finally effectively improve the accuracy of the dialogue system.

Key words: dialogue system, intent detection, slot filling, Stack-Propagation framwork, human-machine interaction

摘要： 在对话系统中的自然语言理解模块中存在着意图检测与插槽填充两个任务,这两个任务之间存在着极强的相关性,即插槽信息的生成高度依赖于意图信息。然而,现有工作大部分将其视为两个独立任务实现,导致对话系统的准确率无法得到进一步提升。为此,针对对话系统中意图检测任务与插槽填充任务之间的相关性信息,在已有工作的基础上提出了端到端的基于Stack-Propagation(堆栈传播)思想实现的网络模型。该模型在解码器阶段借鉴并改进了Stack-Propagation框架的思想,即将意图检测的结果加入到插槽填充任务的输入中,使用意图检测的结果去进一步指导插槽填充任务的进行。通过在斯坦福多领域对话数据集上进行实验,证明该模型不仅可以充分利用意图检测任务与插槽填充任务之间的相关性信息,还可以通过联合学习达到相互促进的效果,最终有效提高对话系统的准确率。

关键词: 对话系统, 意图检测, 插槽填充, 堆栈传播框架, 人机交互