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 inefficient in that
they predict the dialogue state at every turn from scratch. Here, 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. Our method decomposes DST into two
sub-tasks and guides the decoder to focus only on one of the tasks, thus
reducing the burden of the decoder. This enhances the effectiveness of training
and DST performance. Our SOM-DST (Selectively Overwriting Memory for Dialogue
State Tracking) model achieves state-of-the-art joint goal accuracy with 51.72%
in MultiWOZ 2.0 and 53.01% in MultiWOZ 2.1 in an open vocabulary-based DST
setting. In addition, we analyze the accuracy gaps between the current and the
ground truth-given situations and suggest that it is a promising direction to
improve state operation prediction to boost the DST performance.

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。