解耦表示与知识用于少样本意图分类和槽填充
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
AI总结：
针对少样本意图分类和槽填充，提出解耦通用语义表示与领域特定知识迁移的方法，通过捕获意图-槽位和槽位-槽位关系，在Snips和FewJoint上达到最优性能。
摘要：
少样本意图分类和槽填充是重要但具有挑战性的任务，因为精细标注数据的稀缺。因此，现有工作首先在具有充足标注数据的源领域上训练模型，然后将模型迁移到仅有少量标注数据的目标领域。然而，整体经验迁移通常会遭受源领域和目标领域之间存在的差距。例如，迁移与领域特定知识相关的经验是困难的。为了解决这个问题，我们提出了一种新方法，明确地将通用语义表示相关经验的迁移与领域特定知识相关经验的迁移进行解耦。具体而言，对于领域特定知识相关的经验，我们设计了两个模块分别捕获意图-槽位关系和槽位-槽位关系。在Snips和FewJoint数据集上的大量实验表明，我们的方法达到了最先进的性能。该方法在1-shot设置下将联合准确率指标从27.72%提高到42.20%，在5-shot设置下从46.54%提高到60.79%。
英文摘要：
Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available. However, experience transferring as a whole usually suffers from gaps that exist among source domains and target domains. For instance, transferring domain-specific-knowledge-related experience is difficult. To tackle this problem, we propose a new method that explicitly decouples the transferring of general-semantic-representation-related experience and the domain-specific-knowledge-related experience. Specifically, for domain-specific-knowledge-related experience, we design two modules to capture intent-slot relation and slot-slot relation respectively. Extensive experiments on Snips and FewJoint datasets show that our method achieves state-of-the-art performance. The method improves the joint accuracy metric from 27.72% to 42.20% in the 1-shot setting, and from 46.54% to 60.79% in the 5-shot setting.
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。
