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

Hao Tang, Donghong Ji, Qiji Zhou

Abstract: 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.