论文标题
带有标签语义注入的动态图形交互式框架，以了解口语
A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
论文作者
论文摘要
由于它们更接近复杂的现实世界情景，因此多数字检测和插槽填充关节模型正在增加牵引力。但是，现有方法（1）专注于确定两个任务中的话语和单件编码标签之间的隐式相关性，同时忽略明确的标签特征； （2）直接合并每个令牌的多信息信息，这可能导致由于引入无关意图而导致的插槽预测不正确。在本文中，我们提出了一个称为DGIF的框架，该框架首先利用标签的语义信息为模型提供了其他信号和丰富的先验。然后，构建了一个多粒料交互式图，以模型与插槽之间的模型相关性。具体而言，我们提出了一种基于标签语义的注入来构建交互式图的新方法，该方法可以自动更新图表以更好地减轻误差传播。实验结果表明，我们的框架明显胜过现有的方法，比Mixatis数据集上的先前最佳模型的相对改善的总体准确性相对提高了13.7％。
Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations between utterances and one-hot encoded labels in both tasks while ignoring explicit label characteristics; (2) directly incorporate multi-intent information for each token, which could lead to incorrect slot prediction due to the introduction of irrelevant intent. In this paper, we propose a framework termed DGIF, which first leverages the semantic information of labels to give the model additional signals and enriched priors. Then, a multi-grain interactive graph is constructed to model correlations between intents and slots. Specifically, we propose a novel approach to construct the interactive graph based on the injection of label semantics, which can automatically update the graph to better alleviate error propagation. Experimental results show that our framework significantly outperforms existing approaches, obtaining a relative improvement of 13.7% over the previous best model on the MixATIS dataset in overall accuracy.
