Efficient Dialogue State Tracking by Selectively Overwriting Memory

Recent works in dialogue state tracking (DST) focus on an open vocabulary-based setting to resolve scalability and generalization issues of the predefined ontology-based approaches. However, they are computationally inefficient in that they predict the dialogue state at every turn from scratch. In this paper, we consider dialogue state as an explicit fixed-sized memory, and propose a selectively overwriting mechanism for more efficient DST. This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations. Moreover, reducing the burden of the decoder by decomposing DST into two sub-tasks and guiding the decoder to focus only one of the tasks enables a more effective training and improvement in the performance. As a result, our proposed SOM-DST (Selectively Overwriting Memory for Dialogue State Tracking) achieves state-of-the-art joint goal accuracy with 51.38% in MultiWOZ 2.0 and 52.57% in MultiWOZ 2.1 in an open vocabulary-based DST setting. In addition, a massive gap between the current accuracy and the accuracy when ground truth operations are given suggests that improving the performance of state operation prediction is a promising research direction of DST.