# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 4 Experimental Results and Analysis
### Experimental Result Analysis  
To evaluate the effectiveness of the CIMKG model quantitatively, the \(F_{1}\) score was used to assess the slot filling performance of the model. The accuracy was used to evaluate the intent detection capability of the model. Furthermore, the overall accuracy is adopted to measure the sentence-level semantic frame parsing, which represents the percentage of utterances in which both the intent labels and slot labels are correctly identified out of the total number of samples. Additionally, to fully demonstrate the effectiveness of the CIMKG model, Joint BERT and BERT-DCA-Net [12] were introduced as baseline models to compare their performance with that of the CIMKG model in domain-specific intent detection and slot filling. The experimental results are listed in Table 2.  
As shown in Table 2, the proposed CIMKG model outperformed the baseline models in terms of \(F_{1}\) score, accuracy, and overall accuracy. Specifically, not only do the individual metrics of \(F_{1}\) score and accuracy outperform those of the baseline models, but the overall accuracy is also higher. This indicates that the CIMKG model is capable of achieving a deeper understanding of semantic information in domain-specific utterances, thereby further enhancing the overall performance of the joint task of intent detection and slot filling. Through a comparison between the Joint BERT and CIMKG models, it can be observed that the overall performance of the CIMKG model in processing short utterances within specialized domains surpasses that of Joint BERT. There are primarily two reasons for this superiority. Firstly, the CIMKG model enhances the ability of utterance representation by incorporating domain-specific knowledge into the utterance through the integration of a knowledge graph. According to the Slot (\(F_{1}\)) results in Table 2, the experimental outcomes demonstrate that the CIMKG model outperforms Joint BERT in both the medical and construction safety domains. This indicates that the injection of knowledge graph triples into utterances can effectively address the challenges of identifying specialized or easily confused terminology within specialized domains. Secondly, the CIMKG model adopts a collaborative interactive approach for simultaneous bidirectional communication and guidance between intent detection and slot filling, which is superior to the method employed by Joint BERT, which relies solely on parameter sharing to implicitly learn the correlation between intent detection and slot filling. While slots are being identified, the results of slot detection also serve as guiding information for the intent recognition process, and, similarly, the results of intent detection have an impact on the identification of slot filling. Similar reasons can also be observed from the comparison of experimental results between Joint BERT and BERT-DCA-Net in Table 2. When compared to BERT-DCA-Net, although both BERT-DCA-Net and CIMKG adopt a collaborative interactive mode for joint \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Medical**} & \multicolumn{3}{c}{**Construction Safety**} \\ \cline{2-7}  & **Slot (\(F_{1}\))** & **Intent** & **Overall** & **Slot (\(F_{1}\))** & **Intent** & **Overall** \\  & **(Acc)** & **(Acc)** & **(Acc)** & **(Acc)** & **(Acc)** \\ \hline Joint BERT & 97.82 & 72.82 & 64.56 & 92.61 & 99.30 & 85.61 \\ BERT-DCA-Net & 97.99 & 73.38 & 64.79 & 97.87 & 99.26 & 86.03 \\ \hline
**CIMKG** & **98.16** & **73.56** & **65.86** & **98.02** & **99.26** & **87.13** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results. intent detection and slot filling, the incorporation of a knowledge graph in the CIMKG model enables it to better enhance the overall performance of intent detection and slot filling in specialized domains. Therefore, the CIMKG model outperforms BERT-DCA-Net in terms of Slot (\(F_{1}\)), Intent (Accuracy), and Overall (Accuracy).