## 2 Related Work  
### Medical Dialogue System  
The medical dialogue system automatically generates diagnosis and treatment recommendations based on the patient's input regarding their condition. This not only alleviates the shortage of medical resources but also enhances the efficiency of patient consultations. In recent years, medical dialogue systems have made significant progress in tasks such as dialogue generation [3; 4], information extraction [5], classification [6], and sequence annotation [7]. For example, [PERSON] et al. [8] proposed a dual-stream augmented framework for medical dialogue generation, focusing on extracting medical entities and dialogue acts within contextual dialogues. [PERSON] et al. [9] proposed a context-sensitive deep matching model for extracting medical dialogue information, aimed at enhancing the comprehension of contextual dialogue information in medical settings. [PERSON] et al. [10] developed a retrieval-based medical Q&A system aimed at enhancing the accuracy of answers provided in medical Q&A scenarios. This paper investigates the joint tasks of intent detection and slot filling in medical dialogue. These tasks essentially involve classification and sequence labeling. Therefore, the following will provide a detailed introduction to related work on classification and sequence labeling tasks.  
For the classification task, [PERSON] et al. [13] proposed a clinical note segmentation scheme to tackle the issue of critical patient information loss in unstructured clinical records. Their model identifies contextual changes and assigns medically relevant headings to each section. [PERSON] et al. [14] evaluated the application of transfer learning techniques for detecting suicide attempts and suicidal beliefs in electronic health records. They fine-tuned publicly available models to assess the effectiveness of transfer learning with five different parameters, aiming to address the challenge of limited resources in clinical applications. [PERSON] et al. [15] proposed a new neural network-based clinical coding architecture. It initially leverages the hierarchical structure of ontologies to form clusters grounded in semantic relationships. Subsequently, the Matcher module assigns probabilities to determine the likelihood of a document belonging to each cluster, addressing the challenge of multi-label classification in medical terminology.  
On the task of sequence annotation, [PERSON] et al. [16] constructed two large datasets of biological pathogens known to cause diseases. They applied manually prescribed indexes to these datasets to identify focal entities and utilized a machine learning approach to distinguish these focal entities from background entities in medicine. [PERSON] et al. [17] first designed a novel natural language processing application aimed at identifying medical terms within electronic health record notes that might pose difficulties for patients to comprehend. Secondly, they introduced a new publicly available dataset and proposed a medical terminology extraction model to tackle the challenge of detecting unfamiliar terms within specific domains. [PERSON] et al. [18] explore knowledge-enriched self-supervision of biomedical entity linking. They leverage readily available domain knowledge to generate self-supervised mention examples from unlabeled text using a domain ontology during training. Additionally, they employ contrastive learning to train a context encoder that addresses the challenge of fuzzy entity boundaries, thereby enhancing the efficiency of entity processing.  
Although there have been studies on classification and sequence annotation tasks, there are very few approaches that are applicable to the joint study of intent detection and slot filling tasks for medical texts, so in this work, this paper investigates the two tasks of intent detection and slot filling in the field of medical dialogues to improve the accuracy of medical dialogues in the diagnostic process.