Slot Transferability for Cross-domain Slot Filling

Abstract

Cross-domain slot filling focuses on using labeled data from source domains to train a slot filling model for target domains. It is of great significance for transferring a dialogue system into new domains. Most of the existing work focused on building a cross-domain transfer model. From the perspective of slots themselves, this paper proposes a model-agnostic Slot Transferability Measure (STM) for evaluating the transferability from a source slot to a target slot, specifically, the degree that labeled data of the source slot is helpful to train the slot filling model for the target slot. We also give a STM-based method for a model to select helpful source slots and their labeled data for a given target slot. Experimental results on multiple existing models and datasets show that our method significantly outperforms state-ofthe-art baselines in cross-domain slot filling.