  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.
