共同指导多目的口语语言理解
IEEE transactions on pattern analysis and machine intelligence
|November 29, 2023
概括
本研究介绍了Co-guiding Net,这是一种用于多目的口语理解 (SLU) 的新型模型. 它通过模拟双向任务相关性来改进意图预测和插槽填充,显著超过之前的方法.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于图形的模型显示,通过模拟意图到插槽指导,对多意图口语理解 (SLU) 有希望.
- 现有的方法受到单向引导建模和均图形结构的影响,限制了性能.
- 意图和插槽之间的双向相互关联至关重要,但未得到充分探索.
研究的目的:
- 提出一种新型模型,即Co-guiding Net,它捕捉了多意向SLU中意图预测和插槽填充之间的双向相互关联.
- 引入异质图的注意网络,以改进语义-标签交互的建模.
- 通过监督的对比学习策略来提高绩效.
主要方法:
- 一个两阶段的框架,实现意图和时段任务之间的相互指导.
- 两个异质的图表注意力网络在新的语义标签图表上运行.
- 协同指导-SCL Net 包含单任务和双任务监督对比学习.
主要成果:
- 同导网在多意向SLU任务上取得了显著的改进,在MixATIS数据集上相对准确度增加了21.3%.
- 该模型在零射击跨语言场景中表现出强的表现,在9种语言中平均提高了33.5%的最先进状态.
- 不同质的图形和相互指导有效地模拟任务的相互依赖.
结论:
- 拟议的协同指导网络有效地模拟了双向任务相关性,用于增强多意向SLU.
- 不同质的图表注意力网络和对比学习有助于提高表现.
- 该模型显示了跨语言SLU应用的巨大潜力.
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