## 2 Related Works
### Joint Modeling via the Interactive Framework  
The interactive framework widely uses joint intent detection and slot-filling models, which have greatly progressed. Compared with the traditional stacked joint model that learns relevant information through ordered hierarchical transfer, the interactive framework focuses more on the interactive impact of the two tasks of slot filling and intention detection simultaneously during the training process. Where intention and semantic slots, as semantic representations of user behavior, share information about user discourse, information from one task can be used by the other task to mutually improve each other's performance, and contextual information from intention detection and semantic slot filling can provide clues to the other task, making the two tasks perform mutually better and thus optimizing global performance.  
[PERSON] et al. [4] introduced a multi-tasking framework with a slot-gate mechanism for joint multi-intent detection and slot filling. This framework captures features between intents and slots by merging intent information through the treatment of an intent context vector as multiple-intent information. However, this straightforward approach of merging multiple-intent information does not offer detailed intent information at the token level, which is crucial for guiding the slot-filling process. To address this problem, [PERSON] et al. [5] presented the adaptive graph interaction framework (AGIF), which incorporates an intent-slot graph interaction layer to model the robust correlation between slots and intents, and further proposes a global-local graph interaction based on this network GL-GIN [18] to ensure the model runs in parallel and speeds up the inference of the task. In [19], a joint model BIF-SI based on improved multi-intent detection and slot filling is proposed to address the problems that GL-GIN neglects slot-to-intent guidance, multi-intent detection tasks incorrectly capture information of other irrelevant intents, and the quality of contextual semantic feature extraction needs to be further improved. Similarly, [20] proposes a collaborative guidance network framework that allows the intent detection and slot-filling tasks to guide each other. Inspired by heterogeneous networks, a heterogeneous bidirectional flow interaction structure for joint multi-intent detection and slot filling was proposed in [21] and utilizes a word-level windowing mechanism to address the local continuity of slot labels, effectively guiding multi-intent detection with higher accuracy. Moreover, multi-intent detection tasks have been applied to different domains, e.g., in agriculture, [PERSON]. [22] proposed a joint model of agricultural intelligent question and answer (AgHA-IDSF) based on an enhanced heterogeneous attention mechanism, which can effectively jointly recognize intentions and slots in agricultural discourse.  
Building upon the aforementioned interaction framework, this paper further introduces a joint model named Auto-HPIF, aiming to investigate multi-intent detection in the Chinese automotive domain using heterogeneous graph parallel interaction networks. The experimental evaluation encompasses three datasets: CADS, along with two publicly available multi-intent datasets, MixATIS and MixSNIPS. To enhance the model's generalization capability in the Chinese automotive domain, this model employs a fine-tuning approach that incorporates pre-trained multi-task learning techniques. This endeavor holds significance for the study of joint intent detection tasks within specific domains.