## 2 Related Works
### Multi-Intent Detection and Slot Filling  
Compared with single-intent detection tasks, multi-intent detection tasks require a more fine-grained classification of the text, which needs to be decomposed into multiple subtasks and classified when there are multiple intents in a text. As illustrated in Figure 1, for example, in the sentence \"Please lower the temperature of the passenger-side air conditioning to 18 degrees and also help me turn off the dashcam\". The first step for the in-automotive system is to determine two intents of the user: air conditioning control and car function control. This level of fine-grained classification aids the system in better understanding user intent, thereby enabling accurate differentiation and processing among multiple tasks. As deep neural networks continue to advance, deep learning techniques have become the mainstream approach in multi-intent detection. Article [4] uses a linear chain conditional random field (CRF) classifier to achieve multi-intent detection; however, this approach is prone to errors in the propagation phase. Paper [8] combines structural features and a convolutional neural network (CNN) to propose a multi-intent detection model for multi-intent detection by first calculating the distance matrix DIST as an input layer to highlight the features of the sentence and, after passing through a convolutional and pooling layer, and after a fully-connected and softmax layer to fully connect the feature elements, the task type is outputted by softmax. The probability of the output vector is judged by the score. A score threshold is established to identify sentences that exhibit the intention to complete the multi-intent classification task. In the in-automotive domain, [PERSON] et al. [9] examined the utilization of LSTM and GRU for intention detection alongside tasks related to semantic parsing. Given that hierarchical LSTM does not fully utilize contextual information, [PERSON] et al. [10] proposed a multi-task model using bidirectional long short-term memory (BiLSTM) and bidirectional-gated recurrent unit (BiGRU) where the input sentence can be considered as a sequence and the global representation of the GRU and LSTM learning sequence is used to recognize intentions. Although the above intention detection models achieve good recognition results, they do not solve the problem of recognizing multiple intentions in the in-automotive domain. Slot filling is often considered as a sequence annotation task that annotates discourse using a finer granularity to associate certain parts of discourse with predefined slot markers. Traditional slot-filling methods are usually based on conditional random fields (CRFs) [11] of RNNs and other [12] deep learning methods. In recent years, attention mechanisms have also been widely used in NLP tasks, e.g., the literature [13] incorporates attention mechanisms in encoder-decoder models and uses LSTM networks for slot-filling tasks. The literature [14] combines pointer networks and attention mechanisms to improve slot-filling methods. Despite the good results achieved by the above slot-filling methods, these methods require an extensive corpus of labeled data, so pre-trained language models are also tested to be applied to help train slot-filling models. The literature [15] proposes a WFST-BERT model combining the BERT architecture with a weighted finite-state sensor (WFST), which uses the linguistic representation capability of BERT to generate contextual representations  
Figure 1: An example of an NLU utterance with multi-intent and slot annotation (IBO format). and improves generalization; the literature [16] focuses on zero-sample learning and applies a momentum comparison approach and a BERT initialization model encoder to accurately capture semantic patterns; In reference [17], a pre-trained BERT model was employed as a semantic feature extractor, resulting in enhanced model generalization.