ECDG-DST: A dialogue state tracking model based on efficient context and domain guidance for smart dialogue systems

Abstract

Dialogue state tracking (DST) is an important component of smart dialogue systems, with the goal of predicting the current dialogue state at conversation turn. However, most of the previous works had problems with storing a large amount of data and storing a large amount of noisy information when the conversation takes many turns. In addition, they also overlooked the effect of the domain in the task of dialogue state tracking. In this paper, we propose ECDG-DST ¹ (A dialogue state tracking model based on efficient context and domain guidance) for smart dialogue systems, which preserves key information but retains less dialogue history, and masks the domain effectively in dialogue state tracking. Our model utilizes the efficient conversation context, the previous conversation state and the relationship between domains and slots to narrow the range of slots to be updated, and also limit the directions of values to reduce the generation of irrelevant words. The ECDG-DST model consists of four main components, including an encoder, a domain guide, an operation predictor, and a value generator. We conducted experiments on three popular task-oriented dialogue datasets, Wizard-of-Oz2.0, MultiWOZ2.0, and MultiWOZ2.1, and the empirical results demonstrate that ECDG-DST respectively improved joint goal accuracy by 0.45 % on Wizard-of-Oz2.0, 2.44 % on MultiWOZ2.0 and 2.05 % on MultiWOZ2.1 compared to the baselines. In addition, we analyzed the scope of the efficient context through experiments and validate the effectiveness of our proposed domain guide mechanism through ablation study.