End-to-end masked graph-based CRF for joint slot filling and intent detection

Slot filling and intent detection are the basic and crucial fields of natural language processing (NLP) for understanding and analyzing human language, owing to their wide applications in real-world scenarios. Most existing methods of slot filling and intent detection tasks utilize linear chain conditional random field (CRF) for only optimizing slot filling, no matter the method is a pipeline or a joint model. In order to describe and exploit the implicit connections which indicate the appearance compatibility of different tag pairs, we introduce a graph-based CRF for a joint optimization of tag distribution of the slots and the intents. Instead of applying the complex inference algorithm of traditional graph-based CRF, we use an end-to-end method to implement the inference, which is formulated as a specialized multi-layer graph convolutional network (GCN). Furthermore, mask mechanism is introduced to our model for addressing multi-task problems with different tag-sets. Experimental results show the superiority of our model compared with other alternative methods. Our code is available at https://github.com/tomsonsgs/e2e-mask-graph-crf.