# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 4 Experiments
### Comparative Experiments  
To scientifically evaluate the performance, several baseline models were selected for comparative experiments and compared with our model. The baseline models are listed below. Slot-Gated [2] focuses on learning the relationship between intents and slots through slot gates, leading to better IDSF task performance in global optimization. SF-ID Network [14] guarantees a bi-directional correlation between intents and slots through the SF sub-network and ID sub-network. By using an innovative iterative mechanism, the model mutually promotes these two tasks during training, leading to better global optimization. CM-Net [15] utilizes a novel collaborative memory network that first obtains slot-specific and intent-specific features from memory collaboratively and then uses these features for modeling. By leveraging the co-occurrence relationship between slots and intents, it achieves better global optimization when training end-to-end models. Stack-Propagation Framework [4] proposes a framework that incorporates token-level intent detection to capture intent semantic knowledge by directly using the intent as input for slot filling. This approach better integrates intent information through a joint model of stack propagation and effectively reduces error propagation, thus improving overall model performance. Co-Interactive Transformer [8] introduces a Co-Interactive module that establishes a two-way connection between IDSF. Through a specific mutual attention mechanism, intents and slots can pay attention to each other's information, thus enabling deep interaction between intents and slots and improving overall task performance. The results of the comparison experiments are shown in Table 1.  
Overall, the SG framework model significantly outperforms the benchmark model. On the ATIS dataset, the SG framework improves the F1 score on the slot filling task by about 1% compared to the other models. Similarly, it improves the correctness of intent detection by about 1%, and improves the overall correctness by about 2%. On the SNIPS dataset, the SG framework improves the F1 score on the slot filling task by about 1.5% compared to the other models. Similarly, the correctness of intent detection improves by about 1%, and the overall correctness impressively improves by about 1%. The results show that the SG framework achieves better results on both the IDSF tasks. Most importantly, the design of the 12 dynamic gates ensures that the intent and slot features can effectively interact with each encoder layer of BERT to capture richer contextual information. In this way, our framework not only takes advantage of the powerful representational capabilities of BERT, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**ATIS**} & \multicolumn{3}{c}{**SNIPS**} \\ \cline{2-7}  & **Slot (F1)** & **Intent (Acc)** & **Overall (Acc)** & **Slot (F1)** & **Intent (Acc)** & **Overall (Acc)** \\ \hline Slot-Gated * [2] & 88.72 & 95.22 & 74.80 & 94.20 & 93.66 & 82.90 \\ SF-ID Network * [14] & 90.55 & 96.15 & 78.84 & 94.72 & 96.58 & 86.24 \\ CM-Net * [15] & 93.40 & 96.75 & 84.53 & 95.14 & 96.24 & 85.47 \\ Stack-Propagation * [4] & 94.25 & 97.05 & 86.95 & 95.44 & 97.15 & 86.65 \\ Co-Interactive Transformer * [8] & 95.65 & 96.81 & 86.45 & 95.20 & 98.11 & 88.92 \\ SG framework * & 96.55 & 97.54 & 88.20 & 95.60 & 98.43 & 89.30 \\ \hline \hline \end{tabular} All asterisks (*) denote results run on the local machine.  
\end{table} 
Table 1: Performance of comparative experiments running on our same local machine. but also ensures that these representational capabilities are fully utilized throughout the model. Dynamic gates can automatically adjust the weights based on the input features, thus realizing adaptive feature fusion.