# Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification
## 4 Experiments
### Ablation Studies  
We conduct ablation studies to verify the effectiveness of the margin proxy and the shared proxy, which are components of the Advanced Proxy-Anchor loss. Each ablation model is as follows:  
* w/o Shared is a model in which the shared proxy is removed from the proposed method. That is, in this setting, all samples are learned in a single-label state, not a multi-label state;
* w/o margin is a model without the margin proxy, and it is a variant used to check the difference between setting the margin as a vector and as a scalar. This model uses the margin as a trainable scalar value, not as an embedding vector. * w/o ALL is a setting where all the components proposed in this study are removed, and where OOD intent classification is performed with the Classic Proxy-Anchor loss setting [15]. As can be observed in Table 3, our method outperforms in all settings. We can summarize the results of the ablation studies as follows: First, the w/o Margin model shows very poor results in the datasets aside from the StackOverflow dataset. This means that the w/o Margin model face difficulty in determining the decision boundary for OOD intent classification, suggesting that it is effective to initialize the margin with a high-dimensional vector. Compared to the proposed model, the w/o Shared model exhibits an overall performance degradation, and in particular, the F1-OOD has a significant decline. These results demonstrate that the newly introduced multi-label method using a shared proxy is effective in detecting OOD intent. Comparing w/o ALL and baselines through Tables 2 and 3, we can observe that the w/o ALL model outperforms many of the baseline models. Altogether, these findings suggest that the Proxy-Anchor loss framework itself is effective in the OOD intent classification task. \begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**CLINC**} & \multicolumn{4}{c}{**StackOverflow**} & \multicolumn{4}{c}{**BankKING**} \\ \cline{3-14}  & **Methods** & **Acc-** & **F1-** & **F1-** & **F1-** & **Acc-** & **F1-** & **F1-** & **F1-** & **Acc-** & **F1-** & **F1-** & **F1-** \\  & **ALL** & **ALL** & **OOD** & **IND** & **ALL** & **ALL** & **OOD** & **IND** & **ALL** & **ALL** & **OOD** & **IND** \\ \hline \multirow{11}{*}{75\%} & MSP & 74.07 & 82.38 & 59.08 & 82.59 & 72.17 & 77.95 & 33.96 & 80.88 & 75.89 & 83.60 & 39.23 & 84.36 \\  & DOC & 78.73 & 83.59 & 72.87 & 83.69 & 68.91 & 75.06 & 16.76 & 78.95 & 76.77 & 83.34 & 50.60 & 83.91 \\  & OpenMax & 76.80 & 73.16 & 76.35 & 73.13 & 74.42 & 79.78 & 44.87 & 82.11 & 77.45 & 84.07 & 50.85 & 84.64 \\  & SCL & 76.50 & 82.65 & 66.90 & 82.79 & 79.91 & 84.41 & 63.79 & 85.78 & 78.45 & 84.09 & 56.19 & 84.57 \\  & GOT & 72.65 & 81.49 & 54.11 & 81.73 & 77.76 & 81.85 & 52.80 & 83.79 & 77.11 & 83.36 & 48.30 & 83.97 \\  & LMCL & 83.71 & 86.23 & 81.15 & 86.27 & 72.33 & 78.28 & 37.59 & 81.00 & 78.52 & 84.31 & 58.54 & 84.75 \\  & ADB & 86.32 & 88.53 & 83.92 & 88.58 & 82.78 & 85.99 & 73.86 & 86.80 & 81.08 & 85.96 & 66.47 & 86.29 \\  & Outlier & 88.08 & 89.43 & 86.28 & 89.46 & 81.71 & 85.85 & 65.44 & 87.22 & 81.07 & 86.98 & 60.71 & 87.47 \\  & ODIST & 87.70 & 89.30 & 85.86 & 89.33 & 83.75 & 86.88 & 75.21 & 87.66 & 82.79 & 86.94 & 71.95 & 87.20 \\  & FM + ASoul & **89.88** & **91.38** & **88.21** & **91.41** & 85.00 & 87.90 & 75.76 & 88.71 & 84.47 & 88.39 & 72.64 & 88.66 \\  & Ours & 89.41 & 91.13 & 87.49 & 91.16 & **86.68** & **89.12** & **78.68** & **89.82** & **84.62** & **88.67** & **73.00** & **88.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._