# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
###### Abstract  
Intent detection and slot filling tasks share common semantic features and are interdependent. The abundance of professional terminology in specific domains, which poses difficulties for entity recognition, subsequently impacts the performance of intent detection. To address this issue, this paper proposes a co-interactive model based on a knowledge graph (CIMKG) for intent detection and slot filling. The CIMKG model comprises three key components: (1) a knowledge graph-based shared encoder module that injects domain-specific expertise to enhance its semantic representation and solve the problem of entity recognition difficulties caused by professional terminology and then encodes short utterances; (2) a co-interactive module that explicitly establishes the relationship between intent detection and slot filling to address the inter-dependency of these processes; (3) two decoders that decode the intent detection and slot filling. The proposed CIMKG model has been validated using question-answer corpora from both the medical and architectural safety fields. The experimental results demonstrate that the proposed CIMKG model outperforms benchmark models. co-interactive module; knowledge graph; intent detection; slot filling +
Footnote †: journal: Applied sciences  
0000-0001-6547-3399/2000/00000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-000000-000000-0000000-00000000000000000000000000000000000000000000000000000 In this example, when the dialogue system recognizes the user's intent category as \"Request-Etiology\", the detected intent guides the system to focus on the \"HbA1c\", which is then classified into the named entity category of the \"Medical_Examination\". Simultaneously, after the system recognizes \"HbA1c\" as a specific medical examination, it further detects the user's intent to inquire about the specific reasons for any abnormalities in the examination results. This example demonstrates that intent detection can facilitate the determination of the slot type, and the information provided by the slot can, in turn, help the dialogue system more accurately detect the user's intent. Owing to the strong correlation between intent detection and slot filling, some joint models based on multi-task learning frameworks have been widely proposed to simultaneously tackle both intent detection and slot filling [8]. However, certain existing joint models [9; 10; 11] exclusively employ intent information in a unidirectional fashion to optimize slot filling while overlooking the potential guiding influence that slot information could exert on intent detection [12]. In response to the shortcomings of existing joint models, [PERSON] et al. [12] introduced a co-interactive transformer specifically designed for joint intent detection and slot filling. This transformer utilizes the bidirectional information flow between intent detection and slot filling to model the relationship between the two tasks. However, in short utterances lacking domain-specific contexts, challenges emerge in terms of ambiguous intent detection and difficulties in domain-specific entity recognition, specific abbreviations, and easily confused entity recognition. For example, in response to a user's request, such as \"What causes high Hb1c levels in patients?\", a general-domain model may not understand the meaning of \"HbA1c\", which can easily result in errors in entity recognition. In fact, \"HbA1c\" is a type of glycated hemoglobin in the medical field that reflects the average blood glucose level over the past 2 to 3 months. In the utterance \"What preparations are needed before undergoing a BMP test?\", \"BMP\" refers to the basic metabolic panel, which belongs to the category of \"Medical_Examination\". However, in the utterance \"What is the role of BMP in bone repair?\", \"BMP\" refers to a bone morphogenetic protein, which is the category of \"Drug\". Clearly, the meaning of \"BMP\" differs in the two aforementioned utterances.