# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 2 Related Work  
Traditional dialogue state tracking methods heavily rely on extracting triplet information from natural language understanding to predict the current dialogue state [11; 12; 13]. However, these methods rely on manually crafted rules and domain-specific delexicalization, which incurs significant manual effort and limits the models' generalization ability in multi-domain conversations. With the rise of deep learning, researchers have applied various deep neural networks to the task of dialogue state tracking. [PERSON] [14] utilizes a deep fully connected neural network to calculate the probabilities of all slot values for each slot, predicting the slot values. [PERSON] [1] proposes the Neural Belief Track (NBT) model, which leverages representation learning to embed candidate slot-value pairs and dialogue embeddings into dense word vectors, and infers their representations during decoding to determine whether the slot-value pair appears in the dialogue. However, the scalability of the above methods is \begin{table}
\begin{tabular}{c p{284.5 pt}} \hline User: & Can you help me find a place to eat in the expensive price range in the south part of town? \\ \hline Sys: & What about the Cambridge chop house. The address is: 1 kings Parade. Do you like me to book a table? \\ User: & Yes! Can you book for 2 people at 14:15 on this Sunday? \\ \hline Sys: & I was able to book your party of 2 into The Chop House on Sunday at 14:15 Reference: MUWLBLLW. \\ User: & Actually yes I will be needing somewhere to stay too please. A 3 star hotel in the same area and price range as my restaurant. \\ \hline Sys: & I have one matching result. It is the lensfield hotel. Do you want to reserve a room? \\ User: & Yes please, two people for two nights on Sunday. \\ \hline \end{tabular}
\end{table} 
Table 1: In a task-oriented multi-turn dialogue example, the values of the slots “hotel-pricerange” and “hotel-area” should correspond to the same values as the slots “restaurant-pricerange” and “restaurant-area”. limited, and their performance is suboptimal. TRADE [15] combines the copy mechanism and generation mechanism, weighting the probability distributions obtained from both mechanisms at each decoding step of a slot value and using recurrent neural networks to capture semantic correlations between dialogue contexts to improve prediction capabilities. Refs. [16; 17; 18] treat the dialogue state as a selective rewrite memory structure, a table, and state momentum to enhance the model's ability to update and correct slot values. [PERSON] [6] proposes the simultaneous use of three copy mechanisms to fill in slots, addressing the issue of open vocabulary. [PERSON] [19] adopts a reading comprehension approach for state updates. In [20], an attention-based pointer network is used to copy and slot values from the dialogue context. [PERSON] [21] proposes the DiCoS-DST model, which implements a dynamic selection method for dialogue history. Although the aforementioned methods partially address the issue of scalability, they treat the modeling of slots independently without considering the interdependencies among slots in different domains. In recent years, pre-trained language models [22; 23] have demonstrated excellent semantic encoding capabilities in downstream tasks and have received significant attention. For example, the SUMBT model proposed by [PERSON] [3] encodes slot representations and dialogue representations using BERT, and models the relationship between slots and dialogues through attention mechanisms. [PERSON] [24] uses a fusion network that combines context and pattern graphs to model dialogue states. [PERSON] [25] has proposed a novel span-selective prediction layer and adopted linear attention encoding. Although [3; 24; 25] modeled the relationship between slots and dialogue history, they overlooked the correlations among slots. [PERSON] [26] explores the hierarchical semantics of ontologies and enhances the relationships between slots through masked hierarchical attention.