## 1 Introduction  
As medical needs increase, intelligent consultation systems [1] have become one of the most effective solutions to address the issue of insufficient healthcare resources. The medical dialogue system is a typical intelligent consultation system [2] (e.g., medical robot and intelligent online consultation), which automatically generates diagnosis and treatment recommendations based on the patient's input, thereby improving the efficiency of medical services. Moreover, it significantly alleviates the issue of patient difficulty in accessing medical care. In medical dialogue systems, natural language understanding is a fundamental task that mainly consists of two tasks, namely, intent detection and information slot filling. Intent detection categorizes the purpose of the patient's inquiry text, while information slot filling assigns precise slot labels to each token in the text, accurately identifying the patient's symptoms. Taking the sentence \"[\"[\"[\"]]\" (Clear nose is just a little bit) as an example, as illustrated in Figure 1, natural language understanding can identify the patient's intent of asking the question as 'Inform-Symptom', and obtain the slot labels for the symptoms (i.e., B-Symptom, I-Symptom, I-Symptom, O, O, O, O, O). In the slot labels, 'B-Symptom' and 'I-Symptom' are the slots required for the slot filling task and contain information about the patient's symptoms. If the intent of the intent detection task is classified incorrectly, it will affect the identification of the correct intent of the patient in the process of medical consultation, and if the slot filling task slot labels cannot be obtained completely, it will lead to the omission of the correct key information in the process of patient consultation, which will result in the medical misdiagnosis, wrong diagnosis, and other situations. In recent years, medical dialogue systems have made significant progress in many 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. This framework extracts medical slot entities and dialogue intent actions (e.g., diagnosing and prescribing medication) and models their transformations using entity-centric graph streams and sequential act streams, respectively, to improve the accuracy of the next round of medical dialogue generation. [PERSON] et al. [9] proposed a context-sensitive deep matching model for medical dialogue information extraction. This model employs a multi-view channel that utilizes multiple mask templates to capture keyword information within dialogues. Additionally, it incorporates a two-way attention mechanism to assess crucial slot information across different contexts, thereby enhancing the extraction of contextual dialogue information. [PERSON] et al. [10] developed a retrieval-based medical question answering (Q&A) system that utilizes a pre-trained language model for intent matching and enhances sorting through named entity recognition and knowledge graphs. This approach aims to mine relationships between slotted entities in questions and answers, thereby improving the accuracy of responses in medical Q&A. [PERSON] et al. [11] proposed a novel method named XTARS, which integrates traditional BERT-based classification with modern zero- and few-shot learning techniques. This method addresses the automated identification and categorization of terms in medical corpora, specifically for various intent types in medical texts. Their approach significantly enhances the performance of intent classification in medical contexts. Building on this foundation, [PERSON] et al. [12] developed a novel synonym generalization framework. This approach employs span-based prediction to identify biomedical concepts within input text. Additionally, they introduce two regularization terms aimed at resolving boundary issues in biomedical slot entity extraction.