Toward Open-domain Slot Filling via Self-supervised Co-training
This addresses the need for scalable slot filling in conversational AI systems by reducing reliance on manual labeling, though it is an incremental improvement over existing weak supervision methods.
The paper tackles the problem of open-domain slot filling without manually labeled training data by proposing a self-supervised co-training framework called SCot, which outperforms state-of-the-art models by 45.57% on SGD and 37.56% on MultiWoZ datasets and achieves performance comparable to fully supervised models.
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.