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We propose a novel Transformer encoder-based architecture with syntacticalknowledge encoded for intent detection and slot filling. Specifically, weencode syntactic knowledge into the Transformer encoder by jointly training itto predict syntactic parse ancestors and part-of-speech of each token viamulti-task learning. Our model is based on self-attention and feed-forwardlayers and does not require external syntactic information to be available atinference time. Experiments show that on two benchmark datasets, our modelswith only two Transformer encoder layers achieve state-of-the-art results.Compared to the previously best performed model without pre-training, ourmodels achieve absolute F1 score and accuracy improvement of 1.59% and 0.85%for slot filling and intent detection on the SNIPS dataset, respectively. Ourmodels also achieve absolute F1 score and accuracy improvement of 0.1% and0.34% for slot filling and intent detection on the ATIS dataset, respectively,over the previously best performed model. Furthermore, the visualization of theself-attention weights illustrates the benefits of incorporating syntacticinformation during training.

Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling