## 5 Experiments
### Experimental Results and Analysis  
The experimental results of all methods on four dataset are shown in Table 5. Based on the above results, we get the following four observations. Our propose method exceeds all baseline methods in most datasets and settings. Compared to the second best methods, MultiDS achieves an average improvement of \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Meta-training episode & 5000 \\ Meta-validation episode & 100 \\ Meta-testing episode & 1000 \\ Learning rate & \(5\times 10^{-3}\) \\ Hyperparameter \(\eta\) & \(1\times 10^{-5}\) \\ Hidden size of MLP in attention translator & 50 \\ Hidden size of BiLSTM in attention translator & 50 \\ Hidden size of MLP in attention fusion & 1 \\ Hidden size of BiLSTM in domain calibrator & 50 \\ Hidden size of MLP in domain calibrator & 768 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings for model training and network architecture. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Clin\(\rightarrow\)Banking**} & \multicolumn{2}{c}{**Clin\(\rightarrow\)Huffpost**} & \multicolumn{2}{c}{**Clin\(\rightarrow\)Hwu**} & \multicolumn{2}{c}{**[PERSON]\(\rightarrow\)Liu**} & \multicolumn{2}{c}{**Average**} \\ \cline{2-13}  & **10-Way-1-** & **10-Way-5-** & **10-Way-1-** & **10-Way-5-** & **10-Way-1-** & **10-Way-5-** & **10-Way-1-** & **10-Way-5-** & **10-Way-1-** & **10-Way-5-** \\  & **Shot** & **Shot** & **Shot** & **Shot** & **Shot** & **Shot** & **Shot** & **Shot** & **Shot** \\ \hline ProtoNet & 55.92 & 78.47 & 23.41 & 41.04 & 64.55 & 85.15 & 58.72 & 76.35 & 50.65 & 70.24 \\ HAT & 53.13 & 76.69 & 24.01 & 40.06 & 63.57 & 83.51 & 57.77 & 74.61 & 49.62 & 68.72 \\ DS-FSL & 53.85 & 82.13 & 29.10 & 46.61 & 62.70 & 86.45 & 57.45 & 82.17 & 50.78 & 74.34 \\ MLADA & 60.23 & 81.02 & 27.37 & 39.13 & 64.38 & 86.29 & 61.63 & 83.43 & 53.40 & 72.47 \\ DualAN & **63.98** & 85.61 & 28.88 & 45.15 & 66.45 & 86.43 & 63.65 & 82.90 & 58.74 & 75.02 \\ MultiDS & 63.84 & **86.29** & **29.71** & **48.20** & **67.72** & **87.69** & **65.09** & **84.99** & **56.59** & **76.79** \\ \hline \multirow{2}{*}{Model} & \multicolumn{2}{c}{Clin\(\rightarrow\)Banking} & \multicolumn{2}{c}{Clin\(\rightarrow\)Huffpost} & \multicolumn{2}{c}{Clin\(\rightarrow\)Hwu} & \multicolumn{2}{c}{Clin\(\rightarrow\)Liu} & \multicolumn{2}{c}{Average} \\ \cline{2-13}  & 15-way-1- & 15-way-5- & 15-way-1- & 15-way-5- & 15-way-1- & 15-way-5- & 15-way-1- & 15-way-5- & 15-way-1- & 15-way-5- \\ \cline{1-1}  & shot & shot & shot & shot & shot & shot & shot & shot & shot \\ \hline ProtoNet & 49.61 & 73.91 & 17.60 & 33.78 & 58.16 & 81.10 & 52.65 & 69.64 & 44.51 & 64.61 \\ HAT & 47.60 & 72.95 & 18.50 & 33.20 & 57.00 & 79.59 & 51.28 & 68.01 & 43.60 & 63.44 \\ DS-FSL & 44.68 & 77.04 & 21.99 & 36.62 & 54.61 & 82.44 & 51.39 & 78.94 & 43.17 & 68.81 \\ MLADA & 54.62 & 76.79 & 20.82 & 32.97 & 57.47 & 81.36 & 54.85 & 78.44 & 46.94 & 67.39 \\ DualAN & **58.58** & 80.87 & 22.83 & 35.15 & 60.98 & 83.03 & 58.15 & 72.77 & 50.14 & 69.71 \\ MultiDS & 58.32 & **81.75** & **23.53** & **40.97** & **62.07** & **84.28** & **59.38** & **81.11** & **50.84** & **72.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of all methods on Clinc\(\rightarrow\)Banking, Clinc\(\rightarrow\)Huffpost, Clinc\(\rightarrow\)Hwu and Clinc\(\rightarrow\)Liu, respectively. The best and second best results are emphasized using bold fonts and underlines, respectively. 2.69%. Compared to the five baseline methods, MultiDS outperforms them by 11.03% in Clinc\(\rightarrow\)Banking, 4.38% in Clinc\(\rightarrow\)Huffpost, 4.38% in Clinc\(\rightarrow\)Hwu and 6.19% in Clinc\(\rightarrow\)Liu on average. These above results illustrate the effectiveness and superiority of MultiDS. Our proposed approach is able to surpass DS-FSL in all datasets and settings, improving by an average of 4.79%.