## 7 Conclusions  
In this study, we propose a multi-level distributional signatures based model, MultiDS, to solve DG-FSTC problem. Firstly, we propose a hierarchical attention generator to translate multi-level distributional signatures into high-quality word-level attentions. We utilize a large news corpus to derive domain-agnostic attention. We extract domain-specific attention and class-specific attention from domain-related and class-related information using ridge regression and attention translator. Secondly, we propose a domain calibrator that uses domain features and domain-specific attention to generate domain calibration vectors. Experimental results show that our method achieves the best average performance on four testing datasets. The effectiveness of each module is verified by ablation experiments. In model stability validation, our model can exceed the baseline method in most cases. In the future, we will try to design better ways to use distributional signatures, not just word frequency.  
Conceptualization, X.W.; Methodology, X.W. and D.C.; Software, X.W. and X.L.; Validation, X.W. and D.C.; Formal analysis, Y.D. and X.L.; Investigation, Y.F.; Resources, Y.D.;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**N-Way-5-Shot on Clinc\(\rightarrow\)Huffpost**} \\ \cline{2-7}  & \(N=5\) & \(N=6\) & \(N=7\) & \(N=8\) & \(N=9\) & \(N=10\) \\ \hline DS-FSL & 61.22 & 57.03 & **54.70** & 50.80 & 48.83 & 46.61 \\ DualAN & 58.45 & 55.21 & 52.36 & 45.97 & 43.90 & 45.15 \\ MultiDS & **62.12** & **58.16** & 53.15 & **52.64** & **50.51** & **48.20** \\ \hline \multirow{2}{*}{Model} & \multicolumn{5}{c}{\(10\)-way-\(K\)-shot on Clinc\(\rightarrow\)Huffpost} \\ \cline{2-7}  & \(K=1\) & \(K=2\) & \(K=3\) & \(K=4\) & \(K=5\) & \(K=6\) \\ \hline DS-FSL & 29.10 & 36.43 & 41.06 & 44.85 & 46.61 & 49.62 \\ DualAN & 28.88 & 35.86 & 40.11 & 43.19 & 45.15 & 46.45 \\ MultiDS & **29.71** & **37.31** & **41.78** & **45.71** & **48.20** & **50.10** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model stability verification on Clinc\(\rightarrow\)Huffpost. The best results are emphasized using bold fonts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Total Time**} & \multicolumn{2}{c}{**Time for**} & **Time for** \\  & & **Meta-Training** & **Meta-Testing** \\ \hline ProtoNet & 1530 s & 1209 s & 321 s \\ HAT & 1753 s & 1448 s & 305 s \\ DS-FSL & **896 s** & **772 s** & 124 s \\ MLADA & 1458 s & 1219 s & 239 s \\ DualAN & 1651 s & 1381 s & 270 s \\ MultiDS & 1051 s & 938 s & **113 s** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the computational time on Clinc\(\rightarrow\)Banking under 10-way-1-shot. The best and second best results are emphasized using bold fonts and underlines, respectively.  
Data curation, X.C.; Writing--review and editing, Y.L.; Supervision, C.X. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation (Grant Nos. 61872298, 61802316, and 61902324), the Sichuan Regional Innovation Cooperation Project (Grant No. 2021 YKQ0008) and Innovation Fund of Postgraduate, Xihua University (Grant No.YCJJ2021030).  
Not applicable.  
The data used in this study is available at [[https://github.com/Willy1005/DG-FSTC-and-DualAN](https://github.com/Willy1005/DG-FSTC-and-DualAN)]([https://github.com/Willy1005/DG-FSTC-and-DualAN](https://github.com/Willy1005/DG-FSTC-and-DualAN)) accessed on 11 October 2022.  
The authors declare no conflict of interest.