# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 2 Related Work
### Joint Models  
Joint models are methods that simultaneously address intent detection and slot filling tasks. By sharing semantic features and model parameters, the joint models exploit the correlations between the two tasks to enhance overall performance. Joint models can be further classified into implicit, unidirectional interrelated, and bidirectional interrelated models [1, 24].  
Implicit joint models are modelling approaches that implicitly learn the correlation between intent detection and slot filling through shared parameters [7, 12]. [PERSON] et al. [25] proposed a neural network model based on the attention mechanism that uses a bidirectional recurrent neural network (BiRNN) as a shared encoder to simultaneously learn intent detection and slot filling tasks. [PERSON] et al. [6] proposed Joint BERT, which concurrently considers the mutual influence between intent detection and slot filling. By combining the loss functions of the two tasks during the fine-tuning phase, Joint BERT not only saves training time but also improves the performance of intent detection. However, Joint BERT optimization of the joint loss function suffers from a lack of interpretability [24].  
Unidirectional interrelated models refer to models that use intent information to explicitly guide slot filling or use slot information to explicitly guide intent detection [7]. [PERSON] et al. [9] proposed a slot-gating mechanism to establish a connection between intent detection and slot filling tasks. By integrating the output of intent detection as additional information into the decision-making process of slot filling, the model's understanding of text semantics during entity recognition is enhanced, leading to improved performance on the slot filling task [11, 26]. [PERSON] et al. [11] proposed a Stack Propagation framework that uses intent information to guide the slot filling task to better capture semantic knowledge. [PERSON] et al. [26] proposed JointIDSF for Vietnamese based on Joint BERT [6], which introduces an intent-slot attention layer that explicitly incorporates intent information into slot filling through \"soft\" intent label embedding. However, the above models only consider the information transfer from intent detection to slot filling and do not consider the information transfer in the opposite direction, failing to make full use of the bidirectional interactive information between the intent and slot, resulting in the limited performance of the model for intent detection and slot filling.  
In contrast, bidirectional interrelated models, by considering information transfer in both directions, can better capture the interdependencies between two tasks, thus improving the overall performance of intent detection and slot filling. [PERSON] et al. (2017) proposed a Character-Word Information Interaction Framework (CWIIF) specifically for natural language understanding in the Chinese medical dialogue domain. [PERSON] et al. (2019) proposed a co-interactive module for joint intent detection and slot filling to address the limitations of a single information flow. The core of this model is the interaction between intent representations and slot representations within a co-interactive attention layer, resulting in intent-aware slot representations and slot-aware intent representations. The co-interactive module establishes a bidirectional connection between the two tasks, thereby enhancing the performance of intent detection and slot filling.  
Joint models fully exploit the strong correlations between intent detection and slot filling, which offers superior performance compared to pipeline methods. However, in specific domains, a lack of domain-specific knowledge often leads to errors in entity recognition, particularly for domain entities or abbreviations. Coupled with the influence of joint models, these errors in entity recognition can directly affect intent detection, subsequently causing a decline in the overall performance of both intent detection and slot filling.