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.

该研究提出了一种基于固定大小记忆和有选择的覆盖机制的对话状态跟踪模型，其将DST分解为两个子任务，并引导解码器集中于其中一个任务，以提高DST性能。

通过选择性覆盖记忆实现高效对话状态跟踪