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Intent detection and slot filling are two main tasks for building a spokenlanguage understanding (SLU) system. The two tasks are closely related and theinformation of one task can be utilized in the other task. Previous studieseither model the two tasks separately or only consider the single informationflow from intent to slot. None of the prior approaches model the bidirectionalconnection between the two tasks simultaneously. In this paper, we propose aCo-Interactive Transformer to consider the cross-impact between the two tasks.Instead of adopting the self-attention mechanism in vanilla Transformer, wepropose a co-interactive module to consider the cross-impact by building abidirectional connection between the two related tasks. In addition, theproposed co-interactive module can be stacked to incrementally enhance eachother with mutual features. The experimental results on two public datasets(SNIPS and ATIS) show that our model achieves the state-of-the-art performancewith considerable improvements (+3.4% and +0.9% on overall acc). Extensiveexperiments empirically verify that our model successfully captures the mutualinteraction knowledge.

A Co-Interactive Transformer for Joint Slot Filling and Intent Detection