面向多意图口语理解的协同引导
Co-guiding for Multi-intent Spoken Language Understanding
AI总结：
针对多意图SLU中意图与槽位双向关联建模不足的问题，提出Co-guiding Net及对比学习扩展，在MixATIS和零样本跨语言评测中显著提升性能。
摘要：
近期基于图的多意图SLU模型通过建模意图预测对槽位填充分解的引导，取得了令人瞩目的结果。然而，现有方法存在两点不足：（1）仅建模从意图到槽位的单向引导，而意图与槽位之间实际上存在双向相互关联；（2）采用同构图来建模槽位语义节点与意图标签节点之间的交互，这限制了性能。本文提出一种名为Co-guiding Net的新型模型，该模型通过两阶段框架实现两个任务之间的相互引导。在第一阶段，模型生成两个任务的初始估计标签；随后在第二阶段利用这些标签建模相互引导。具体而言，我们提出了两个异构图注意力网络，分别作用于所提出的两个异构语义标签图，从而有效表示语义节点与标签节点之间的关系。此外，我们进一步提出Co-guiding-SCL Net，利用单任务和双任务语义对比关系。在第一阶段，我们提出单任务监督对比学习；在第二阶段，提出协同引导监督对比学习，在对比学习过程中考虑两个任务的相互引导。在多意图SLU上的实验结果表明，我们的模型大幅优于现有模型：在MixATIS数据集的总体准确率上，相比此前最佳模型取得21.3%的相对提升。我们还在零样本跨语言场景中评估了该模型，结果显示，在全部9种语言上，该模型在总体准确率方面平均可使最先进模型相对提升33.5%。
英文摘要：
Recent graph-based models for multi-intent SLU have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling. However, existing methods (1) only model the unidirectional guidance from intent to slot, while there are bidirectional inter-correlations between intent and slot; (2) adopt homogeneous graphs to model the interactions between the slot semantics nodes and intent label nodes, which limit the performance. In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the mutual guidances between the two tasks. In the first stage, the initial estimated labels of both tasks are produced, and then they are leveraged in the second stage to model the mutual guidances. Specifically, we propose two heterogeneous graph attention networks working on the proposed two heterogeneous semantics label graphs, which effectively represent the relations among the semantics nodes and label nodes. Besides, we further propose Co-guiding-SCL Net, which exploits the single-task and dual-task semantics contrastive relations. For the first stage, we propose single-task supervised contrastive learning, and for the second stage, we propose co-guiding supervised contrastive learning, which considers the two tasks' mutual guidances in the contrastive learning procedure. Experiment results on multi-intent SLU show that our model outperforms existing models by a large margin, obtaining a relative improvement of 21.3% over the previous best model on MixATIS dataset in overall accuracy. We also evaluate our model on the zero-shot cross-lingual scenario and the results show that our model can relatively improve the state-of-the-art model by 33.5% on average in terms of overall accuracy for the total 9 languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
