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DCEN: A Decoupled Context Enhanced Network For Few-shot Slot Tagging

Few-shot slot tagging is an important task in developing dialogue system. Most previous few-shot slot tagging models classify an item according to its similarity to the representation of each class. These models leverage context information implicitly through each words' contextual embedding. However, the entangled language features of words may interfere with context information, misleading the utilization of crucial slot features in few-shot scenario. To tackle these problems, we propose the Decoupled Context Enhanced Network (DCEN) for few-shot slot tagging. Different from previous models, we extract decoupled context explicitly to make full use of slot features contained in the context. Decoupled context includes two parts, local and global decoupled context information. We introduce a local extractor to extract local decoupled context by integrating information from adjacent words, and a global extractor based on transformer to extract global decoupled information by orthogonalization. Experimental results on SNIPS show that our model achieves the state-of-the-art performance with considerable improvements.