A typical conversation comprises of multiple turns between participants where they go back-and-forth between different topics. At each user turn, dialogue state tracking (DST) aims to estimate user's goal by processing the current utterance. However, in many turns, users implicitly refer to the previous goal, necessitating the use of relevant dialogue history. Nonetheless, distinguishing relevant history is challenging and a popular method of using dialogue recency for that is inefficient. We, therefore, propose a novel framework for DST that identifies relevant historical context by referring to the past utterances where a particular slot-value changes and uses that together with weighted system utterance to identify the relevant context. Specifically, we use the current user utterance and the most recent system utterance to determine the relevance of a system utterance. Empirical analyses show that our method improves joint goal accuracy by 2.75% and 2.36% on WoZ 2.0 and MultiWoZ 2.0 restaurant domain datasets respectively over the previous state-of-the-art GLAD model.

提出了一种新颖的 DST 框架，它通过有关特定 slot 值更改的过去话语来识别相关历史上下文，并将其与加权系统话语一起使用以确定相关上下文，实验证明该方法在 WoZ 2.0 和 MultiWoZ 2.0 餐厅领域数据集上比先前的 GLAD 模型提高了2.75％和2.36％的目标准确性。

通过识别相关上下文来改善对话状态跟踪