# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 4 Results and Discussion
### Clustering Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Clustering** & **Homogeneity** & **Completeness** & **V-Measure** \\
**Embeddings** & **Clustering** & **Mean \(\pm\) (SD)** & **Mean \(\pm\) (SD)** & **Mean \(\pm\) (SD)** \\ \hline \multirow{4}{*}{BERT} & K-Means & 67.87 \(\pm\) (0.93) & 62.54 \(\pm\) (0.98) & 65.09 \(\pm\) (0.95) \\  & Agglo–Ward & 67.37 \(\pm\) (1.31) & 62.84 \(\pm\) (1.19) & 65.03 \(\pm\) (1.24) \\  & **Agglo-avg** & **65.47 \(\pm\) (2.04)** & **65.05 \(\pm\) (1.38)** & **65.26 \(\pm\) (1.68)** \\  & Agglo–max & 64.79 \(\pm\) (0.96) & 62.47 \(\pm\) (1.40) & 63.61 \(\pm\) (1.14) \\  & Agglo–single & 25.96 \(\pm\) (0.88) & 82.76 \(\pm\) (1.13) & 39.51 \(\pm\) (0.99) \\ \hline \multirow{4}{*}{RoBERTa} & K-Means & 53.90 \(\pm\) (1.29) & 50.05 \(\pm\) (0.78) & 51.90 \(\pm\) (0.98) \\  & **Agglo–Ward** & **53.32 \(\pm\) (1.68)** & **51.36 \(\pm\) (1.55)** & **52.32 \(\pm\) (1.60)** \\  & Agglo–avg & 49.72 \(\pm\) (1.40) & 51.45 \(\pm\) (0.96) & 50.56 \(\pm\) (1.00) \\  & Agglo–max & 51.57 \(\pm\) (0.84) & 49.86 \(\pm\) (0.74) & 50.70 \(\pm\) (0.76) \\  & Agglo–single & 5.90 \(\pm\) (2.66) & 59.75 \(\pm\) (13.37) & 10.66 \(\pm\) (4.68) \\ \hline \multirow{4}{*}{GPT-2} & K-Means & 63.08 \(\pm\) (2.17) & 59.32 \(\pm\) (1.73) & 61.13 \(\pm\) (1.83) \\  & Agglo–Ward & 62.96 \(\pm\) (0.46) & 58.71 \(\pm\) (0.57) & 60.76 \(\pm\) (0.47) \\  & **Agglo–avg** & **61.35 \(\pm\) (0.81)** & **61.08 \(\pm\) (0.32)** & **61.22 \(\pm\) (0.45)** \\  & Agglo–max & 60.15 \(\pm\) (1.25) & 58.51 \(\pm\) (0.63) & 59.32 \(\pm\) (0.91) \\  & Agglo–single & 41.05 \(\pm\) (2.41) & 72.88 \(\pm\) (0.60) & 52.49 \(\pm\) (2.06) \\ \hline \multirow{4}{*}{XLNet} & **K-Means** & **37.20 \(\pm\) (1.63)** & **34.77 \(\pm\) (1.20)** & **35.94 \(\pm\) (1.39)** \\  & Agglo–Ward & 30.87 \(\pm\) (0.84) & 33.62 \(\pm\) (0.78) & 32.18 \(\pm\) (0.78) \\  & Agglo–avg & 25.95 \(\pm\) (3.31) & 33.34 \(\pm\) (2.09) & 29.15 \(\pm\) (2.83) \\  & Agglo–max & 21.57 \(\pm\) (2.63) & 29.00 \(\pm\) (1.92) & 24.71 \(\pm\) (2.38) \\  & Agglo–single & 3.29 \(\pm\) (1.33) & 53.85 \(\pm\) (8.42) & 6.17 \(\pm\) (2.42) \\ \hline \multirow{4}{*}{ALBERT} & K-Means & 48.64 \(\pm\) (0.73) & 44.35 \(\pm\) (0.72) & 46.40 \(\pm\) (0.70) \\  & Agglo–Ward & 47.85 \(\pm\) (0.51) & 44.10 \(\pm\) (0.29) & 45.90 \(\pm\) (0.37) \\  & **Agglo–avg** & **47.41 \(\pm\) (0.56)** & **46.56 \(\pm\) (0.66)** & **46.98 \(\pm\) (0.41)** \\  & Agglo–max & 46.22 \(\pm\) (0.69) & 43.76 \(\pm\) (0.45) & 44.95 \(\pm\) (0.55) \\  & Agglo–single & 12.44 \(\pm\) (3.16) & 59.33 \(\pm\) (4.79) & 20.46 \(\pm\) (4.44) \\ \hline \multirow{4}{*}{ELECTRA} & **K-Means** & **59.01 \(\pm\) (1.18)** & **55.37 \(\pm\) (1.52)** & **57.13 \(\pm\) (1.34)** \\  & Agglo–Ward & 55.36 \(\pm\) (2.37) & 57.66 \(\pm\) (2.10) & 56.48 \(\pm\) (2.23) \\ \cline{1-1}  & Agglo–avg & 48.92 \(\pm\) (1.96) & 60.21 \(\pm\) (2.50) & 53.94 \(\pm\) (1.57) \\ \cline{1-1}  & Agglo–max & 50.53 \(\pm\) (1.99) & 56.59 \(\pm\) (1.08) & 53.38 \(\pm\) (1.59) \\ \cline{1-1}  & Agglo–single & 14.02 \(\pm\) (5.99) & 77.26 \(\pm\) (8.20) & 23.34 \(\pm\) (9.30) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics (in %) attained by using the models with different clustering algorithms.