# Knowledge Distillation-Based Multilingual Code Retrieval
## 3 Experiments
### Experiment Setup
#### 3.4.6 Experiment Equipment  
The equipment uses 3 RTX 1080 Ti 11 GB, and the training time per epoch is around 200-400 s. The number of epochs for a teacher model or student model is around 20-40. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**CODE** & **QUERY** & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Go**} & \multirow{2}{*}{**Java**} & \multirow{2}{*}{**Javascript**} & \multirow{2}{*}{**Php**} & \multirow{2}{*}{**Python**} & \multirow{2}{*}{**Ruby**} \\ \cline{5-5}
**ENCODE** & & & & & & & & \\ \hline \multirow{11}{*}{SELF-ATT} & \multirow{9}{*}{SELF-ATT} & GO & **0.7756** & 0.5400 & 0.4591 & 0.4552 & 0.5649 & 0.4760 \\  & & Java & 0.6485 & **0.6632** & 0.4806 & 0.5390 & 0.6047 & 0.5157 \\  & & Javascript & 0.5688 & 0.5187 & 0.5304 & 0.4494 & 0.5719 & 0.4816 \\  & & Php & 0.6432 & 0.6005 & 0.5068 & **0.6424** & 0.6915 & 0.5572 \\  & & Python & 0.6397 & 0.5691 & 0.4968 & 0.5602 & **0.7613** & 0.5791 \\  & & Ruby & 0.4849 & 0.4319 & 0.3494 & 0.3673 & 0.5167 & 0.4773 \\  & & ALL & 0.7356 & 0.6350 & 0.5240 & 0.6191 & 0.7177 & 0.5717 \\  & & MPLCS & 0.7472 & 0.6404 & **0.5492** & 0.6079 & 0.7289 & **0.5977** \\ \hline \multirow{11}{*}{CNN} & \multirow{9}{*}{CNN} & GO & **0.7780** & 0.5593 & 0.4767 & 0.4872 & 0.6002 & 0.4943 \\  & & Java & 0.6691 & **0.6776** & 0.5106 & 0.5644 & 0.6446 & 0.5381 \\  & & Javascript & 0.6038 & 0.5510 & 0.5546 & 0.4822 & 0.6148 & 0.5122 \\  & & Php & 0.6718 & 0.6181 & 0.5257 & **0.6539** & 0.7152 & 0.5707 \\  & & Python & 0.6783 & 0.5994 & 0.5140 & 0.5658 & **0.7748** & 0.5918 \\  & & Ruby & 0.5628 & 0.4738 & 0.3937 & 0.4208 & 0.5802 & 0.5239 \\  & & ALL & 0.7405 & 0.6458 & 0.5363 & 0.6268 & 0.7301 & 0.5805 \\  & & MPLCS & 0.7451 & 0.6531 & **0.5656** & 0.6217 & 0.7457 & **0.6111** \\ \hline \multirow{11}{*}{NBOW} & \multirow{9}{*}{NBOW} & GO & **0.6777** & 0.5181 & 0.4256 & 0.4081 & 0.5280 & 0.4420 \\  & & Java & 0.5408 & **0.5981** & 0.4354 & 0.4456 & 0.5414 & 0.4590 \\  & & Javascript & 0.5312 & 0.4844 & 0.4799 & 0.4118 & 0.5031 & 0.4087 \\  & & Php & 0.5645 & 0.5359 & 0.4442 & **0.5569** & 0.5720 & 0.4727 \\  & & Python & 0.5746 & 0.5212 & 0.4334 & 0.4499 & **0.6560** & 0.4987 \\  & & Ruby & 0.4645 & 0.4248 & 0.3465 & 0.3465 & 0.4977 & 0.4539 \\  & & ALL & 0.6466 & 0.5660 & 0.4602 & 0.5251 & 0.6117 & 0.4911 \\  & & MPLCS & 0.6710 & 0.5882 & **0.5024** & 0.5283 & 0.6389 & **0.5369** \\ \hline \multirow{11}{*}{SELF-ATT} & \multirow{9}{*}{NBOW} & GO & **0.7599** & 0.5315 & 0.4594 & 0.4379 & 0.5549 & 0.4647 \\  & & Java & 0.6392 & **0.6571** & 0.4849 & 0.5423 & 0.6021 & 0.5110 \\  & & Javascript & 0.5754 & 0.5136 & 0.5354 & 0.4740 & 0.5600 & 0.4687 \\  & & Php & 0.6391 & 0.5899 & 0.4956 & **0.6424** & 0.6678 & 0.5436 \\  & & Python & 0.6340 & 0.5646 & 0.4944 & 0.5473 & **0.7563** & 0.5646 \\  & & Ruby & 0.4840 & 0.4202 & 0.3476 & 0.3446 & 0.5072 & 0.4704 \\  & & ALL & 0.7266 & 0.6291 & 0.5271 & 0.6175 & 0.7127 & 0.5661 \\  & & MPLCS & 0.7403 & 0.6429 & **0.5604** & 0.6148 & 0.7356 & **0.6022** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MRR for each monolingual model and MPLCS model on different language test sets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SuccessRate@k** & \begin{tabular}{c} **CODE** \\ **ENCODE** \\ \end{tabular}