# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 5 Conclusions and Future Work  
This study proposes the CIMKG, which jointly models intent detection and slot filling based on a knowledge graph. The CIMKG thoroughly addresses the problem of the underperformance of general pretrained language models in intent detection and slot filling within specific domains. Specifically, it not only employs a knowledge graph-based encoder module to tackle the problem of knowledge noise that may arise from the introduction of domain-specific expertise but also utilizes a co-interactive module to enable a bidirectional flow of intent and slot information. This fully exploits the interrelationship between intents and slots, thereby enhancing the overall performance of intent detection and slot filling in specific domains. Experiments were conducted using knowledge graphs in both the medical and construction safety domains and related user question-answer data. The experimental results demonstrate that CIMKG effectively addresses the problems of entity recognition and intent detection errors caused by insufficient prior knowledge in both the medical and architectural safety domains. Furthermore, CIMKG significantly improves intent detection and slot filling performance. However, in this study, the utilization of the IMCS21 dataset, which encompasses 16 categories of intents, renders the intent detection task relatively complex. Despite the adoption of the softmax function to predict the probability distribution across various categories, the multi-categories classification itself exacerbates the difficulty \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 (Acc)** & **Overall (Acc)** & **Slot (\(F_{1}\))** & **Intent (Acc)** & **Overall (Acc)** \\ \hline without intent attention layer & 98.12 & 73.33 & 65.39 & 97.77 & 99.26 & 86.40 \\ without slot attention layer & 98.15 & 73.24 & 65.53 & 97.63 & 99.26 & 85.66 \\ with intent-to-slot & 98.10 & 73.44 & 65.57 & 97.45 & 99.26 & 85.29 \\ with slot-to-intent & 97.98 & 73.33 & 64.91 & 97.58 & 99.26 & 85.65 \\ \hline
**CIMKG** & **98.16** & **73.56** & **65.86** & **98.02** & **99.26** & **87.13** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ablation experiments results. for the model to distinguish between different intents, consequently leading to a relatively low accuracy in Intent (Acc). Furthermore, Overall (Acc) reflects the scenario where both the intents and slots are correctly predicted. Hence, it not only relies on the accuracy of intent detection but is also influenced by the outcome of slot filling. Consequently, the overall experimental results may not be as high as anticipated. In addition, it should be noted that this study focused only on single-intent scenarios. Future research will build upon this foundation to investigate and improve the model's ability to handle multi-intent scenarios. Specifically, a dedicated multi-intent processing module will be designed on the basis of the CIMKG. This module will be capable of identifying multiple intents within an utterance and processing each intent independently, thereby effectively transforming them into single-intent issues. Conceptualization, W.Z. and Y.G. ; methodology, W.Z. and Y.G. ; software, W.Z., Z.X., L.W., S.J., and X.Z. ; validation, W.Z., Z.X., and X.Z. ; formal analysis, W.Z., Y.G., L.W., and S.J. ; resources, Y.G. ; data curation, W.Z., Z.X., S.J., and G.Y. ; writing--original draft preparation, W.Z. and G.Y. ; writing--review and editing, Y.G. All authors have read and agreed to the published version of the manuscript. This work was supported in part by Major Scientific & Technological Projects of Shandong Province under Grant 2021 CXGC011204 and in part by the National Natural Science Foundation of Shandong Province under Grant ZR021 MG054. Not applicable.