# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
###### Abstract  
Dialogue state tracking is an essential component in multi-domain dialogue systems that aims to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample often has limitations and may introduce noise and have high time complexity issues. In this paper, we propose an external slot relation memory-based dialogue state tracking model (ER-DST). By utilizing external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Our method is evaluated on MultiWOZ 2.0 and MultiWOZ 2.1, achieving improvements of 0.23% and 0.39% over the baseline models, respectively, while reducing the complexity of the slot relationship learning component to O(n).  
task-oriented dialogue system; dialogue state tracking; attention mechanism; pre-trained language model +
Footnote †: journal: Medical  
have recognized the importance of modeling slot correlations to some extent [5; 6]. In these works, the correlations between slot names have been considered [7], or prior knowledge has been incorporated [5], where artificially set slot correlation coefficients are assigned a value of 1. However, these approaches only consider the association between slot names.  
To address this issue, [PERSON] [8] proposes the Slot Self-Attentive Dialogue State Tracking (STAR) model, which utilizes both the slot names and the context related to the slots to capture slot relationships more accurately through slot self-attention. It learns the correlations between slots in a fully data-driven manner without any manual effort or prior knowledge. However, it learns the relationships between multi-domain slots from a single sample, which may introduce bias during prediction and potentially involve redundant information from other slots.  
In this work, a dialogue state tracking model, ER-DST, is proposed to enable the learning of relationships between multi-domain slots from global samples. The main contributions of this paper are the following:  
* The model utilizes an external slot relationship memory, consisting of keys and values, to capture the relationship characteristics between slots and learn a dictionary of relationships for multi-domain slots;
* A small-scale filter is employed to reduce the weight of irrelevant slots, thereby excluding interference from other slots;
* By leveraging the external slot relationship memory to learn the most discriminative features of slot relationships, the proposed approach achieves a joint goal accuracy of 54.76% on MultiWOZ 2.0 [9] and 56.75% on MultiWOZ 2.1 [10], while maintaining linear complexity.  
The rest of this paper is organized as follows: Section 2 presents related work; Section 3 introduces the proposed model; experiments are in Section 4; experimental results are in Section 5; and the conclusions are finally presented in Section 6.