Efficient Dialogue State Tracking by Selectively Overwriting Memory
Sungdong Kim, Sohee Yang, Gyuwan Kim, Sang‐Woo Lee · 2020
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-theart joint goal accuracy with 51.72% in Mul-tiWOZ 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. 1