Improving Pointer Network based Dialogue State Tracking via Dual Hierarchical Selective Augmentation

Dialogue state tracking is responsible for predicting the user’s dialogue state during the whole dialogue process. In practical applications, values for different slots exist in individual utterances of the dialog history. With the accumulation of the dialogue history, it becomes extremely difficult to accurately predict slots and corresponding values from the lengthy dialogue history. To solve the problem of the interference caused by lengthy dialogue history, we propose a dual hierarchical selective augmentation method, which makes use of two hierarchical level information selection strategy to generate slot values. In the encoding phase, we first extract word-level matching features between the slot and each dialogue turn, and then build turn-level context relevance. In the decoding phase, first of all, from a global perspective, the dialogue turn information is selected multiple according to the dialogue context and slot, so that the model focuses more on the turn containing slot value. Secondly, our model performs weighted context attention to capture the critical words of dialogue turn from the local view. This dual hierarchical context selection alleviates the interference caused by excessive redundant information in the dialogue history and enhances the judgment ability of the model for vital turns and words. Furthermore, to enhance the copying ability of the model, we use the turn selection-guided pointer network to copy slot values from the dialogue. Experimental results show that our model significantly outperforms multiple baselines on the released MultiWOZ benchmark.