CACHE: MODELING CONTRIBUTION-AWARE CONTEXT HIERARCHICALLY FOR LONG-RANGE DIALOGUE STATE TRACKING

Recently, many studies on dialogue state tracking (DST) based on the copy-augmented encoder-decoder framework have been proposed and have achieved encouraging performance. However, these studies commonly lose earlier information during encoding the long dialogues with RNNs, and have difficulty for the decoder to focus on specific dialogue turns from lengthy context, which causes decreased performance as the dialogue gets longer. In this work, we propose a novel method to model Contribution-Aware Context HiErarchically (CACHE) with a hierarchical encoder and a slot-turn attention module. The hierarchical encoder is designed to prevent information loss by reducing the length of the sequence sent to each encoder. The slot-turn attention module is explored to help the decoder focus on the slot-related dialogue turn information. To evaluate models more appropriately, we introduce a new metric continued joint accuracy considering the prediction accuracy of both current and historical dialogue turns. Experiments on MultiWOZ 2.0 show that CACHE is an effective model for tracking states especially in long context.