Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training data for each new domain. Zero-shot learning and weak supervision approaches, among others, have shown promise as alternatives to manual labeling. Nonetheless, these learning paradigms are significantly inferior to supervised learning approaches in terms of performance. To minimize this performance gap and demonstrate the possibility of open-domain slot filling, we propose a Self-supervised Co-training framework, called SCot, that requires zero in-domain manually labeled training examples and works in three phases. Phase one acquires two sets of complementary pseudo labels automatically. Phase two leverages the power of the pre-trained language model BERT, by adapting it for the slot filling task using these sets of pseudo labels. In phase three, we introduce a self-supervised cotraining mechanism, where both models automatically select highconfidence soft labels to further improve the performance of the other in an iterative fashion. Our thorough evaluations show that SCot outperforms state-of-the-art models by 45.57% and 37.56% on SGD and MultiWoZ datasets, respectively. Moreover, our proposed framework SCot achieves comparable performance when compared to state-of-the-art fully supervised models.

翻译：槽填充是现代对话系统中的关键任务之一。现有文献大多采用监督学习方法，该方法需要为每个新领域标注训练数据。零样本学习和弱监督等方法已显示出作为人工标注替代方案的潜力。然而，这些学习范式在性能上显著逊色于监督学习方法。为缩小这一性能差距并证明开放域槽填充的可能性，我们提出了一种名为SCot的自监督协同训练框架，该框架无需任何领域内人工标注训练样本，分三个阶段运行。第一阶段自动获取两组互补的伪标签。第二阶段利用预训练语言模型BERT的能力，通过这两组伪标签将其适配至槽填充任务。在第三阶段，我们引入自监督协同训练机制，使两个模型以迭代方式自动选择高置信度软标签，从而互相提升对方性能。全面评估表明，SCot在SGD和MultiWoZ数据集上分别以45.57%和37.56%的优势超越现有最优模型。此外，与最先进的全监督模型相比，我们提出的SCot框架达到了可比的性能。