# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 4 Experiments
### Ablation Study  
This section presents a series of ablation experiments conducted to evaluate the individual contributions of each component for the overall performance of our proposed model. Specifically, these experiments aim to validate the effectiveness of various modules in the following configurations:  
Model 1: Intent label attention is used only as a guide for text semantics. With this configuration, it is possible to compare it with a version that employs both intent label attention and slot label attention strategies.  
Model 2: Slot label attention is used only as a guide for text semantics. With this configuration, it is possible to compare it with a version that employs both intent label attention and slot label attention strategies.  
Model 3: Dynamic gates are replaced with 12 MLP layers, corresponding to the 12 encoder layers. This configuration can be compared to the version with the dynamic gates strategy.  
According to the results in Table 2, for both the ATS and SNIPS datasets, we can draw the following conclusions:  
To begin with, using only intent label attention as textual semantic guidance reduces the slot filling F\({}_{1}\) scores by about 0.4% and 0.3%, respectively, the correct rates of intent detection by about 0.7% and 0.5%, respectively, and the overall correct rates by about 1% and 0.5%, respectively. Subsequently, using only slot label attention as text semantic guidance reduces the slot filling F\({}_{1}\) scores by about 0.8% and 0.5%, respectively, the correct rate of intent detection by about 1.2% and 1%, respectively, and the overall correct rate by about 1.2% and 0.7%, respectively. It can be seen that the intent label plays a more crucial role in semantic guidance. The intent label provides a more macroscopic semantic framework for the model, which provides context for specific information in the text, thus helping the model to better interpret this information, whereas the slot label, although useful in some contexts, may rely more on the overall intent information to provide it with semantic context. Notably, the model works best when intent label attention and slot label attention are used jointly. This suggests that while intent labeling alone may be more critical than slot labeling, when the two are combined, they complement each other in capturing the details and overall semantics of the text for optimal performance. Finally, replacing the dynamic gate model with the MLP layer results in a severe performance degradation. Specifically, the F\({}_{1}\) score for slot filling drops by about 1.7% and 1.25%, respectively, the correctness of intent detection drops by about 2% and 1.4%, respectively, and the overall correctness drops by about 2% and 1%, respectively. The experiments demonstrate that this substitution leads to the most significant performance drop among all ablation experiments. This highlights the pivotal function of dynamic gates in influencing model performance. Dynamic gates possess the capability to automatically fine-tune feature weights based  
\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 (F\({}_{1}\))** & **Intent (Acc)** & **Overall (Acc)** & **Slot (F\({}_{1}\))** & **Intent (Acc)** & **Overall (Acc)** \\ \hline Model 1 & 96.14 & 96.88 & 87.25 & 95.33 & 97.95 & 88.75 \\ Model 2 & 95.87 & 96.35 & 87.03 & 95.12 & 97.50 & 88.62 \\ Model 3 & 94.85 & 95.56 & 86.32 & 94.35 & 97.02 & 88.21 \\ SG framework & 96.55 & 97.54 & 88.20 & 95.60 & 98.43 & 89.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments for the proposed model.  
on inputs, facilitating adaptive feature fusion, while the MLP layer in isolation lacks this adaptive capability.