A Slot-Shared Span Prediction-Based Neural Network for Multi-Domain Dialogue State Tracking
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
Abstract
There are a large number of candidate values shared among slots in multi-domain dialogue state tracking (DST). The existing span prediction-based DST methods generally adopt slot-independent value extraction architecture, which ignore the value sharing. Besides, the slot-independent design leads to poor scalability. In this paper, we propose a Slot-shared Span Prediction based Network (SSNet) with a general value extraction module for all slots to tackle these problems. To ensure that the value extraction module is able to distinguish different slots, we introduce a Dynamic Fusion Mechanism (DFM) to extract different slot-aware features. DFM plays the routing role, highlighting different dialogue context tokens for different slots. Specifically, DFM firstly calculates similarity matrixes between the dialogue context and different slots, and then determines important dialogue context token with respect to each slot. Experimental results demonstrate that SSNet outperforms the existing start-of-the-art models on both MultiWOZ 2.1 and MultiWOZ 2.2 datasets.
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Key words
task-oriented dialogue system,dialogue state tracking,slot semantics,feature fusion
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