## 6 A Hybrid Approach
Firstly, the first five models in this table are selected multilingual models from sbert.net ([[https://sbert.net/docs/p](https://sbert.net/docs/p)]([https://sbert.net/docs/p](https://sbert.net/docs/p)) retrained_models.html, accessed on 2 May 2023), which are based on English sentence embedding models and were generated using Knowledge Distillation. Secondly, the next three models in this table are our own models that use both Korean and English training datasets to fine-tune the KLUE-RoBERTa-base model. Finally, the last model is generated by our hybrid approach, which combines two small monolingual models. The experimental results show that our hybrid approach demonstrates consistent performance across the four test datasets and outperforms the other approaches on average. Based on our previous experiments, only two existing models had an average accuracy of 75% or higher: \"Huffon/sentence-klue-roberta-base\" and \"paraphrase-multilingual-mpnet-base-v2\". Therefore, we compare our approach with these models in detail. We compare not only accuracy but also model size, encoding time (CPU/GPU), and time for finding the most similar sentences (CPU/GPU). These elapsed times are crucial in many AI-related applications, particularly in AI educational programming environments. Table 7 shows that our hybrid approach outperforms the existing state-of-the-art approaches in \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Multilingual SBERT Models**} & \multicolumn{5}{c}{**Accuracy (\%)**} \\ \cline{2-6}  & **GPT-ko** & **Paraph-ko** & **GPT-en** & **Paraph-en** & **Average** \\ \hline clip-ViT-B-32-multilingual-v1 & 57.20 & 56.25 & 65.60 & 67.05 & 61.53 \\ distiluse-base-multilingual-cased-v2 & 51.35 & 65.10 & 60.55 & 74.60 & 62.90 \\ distiluse-base-multilingual-cased-v1 & 56.00 & 73.70 & 61.00 & 75.30 & 66.50 \\ paraphrase-multilingual-MiniLM-L12-v2 & 74.40 & 62.10 & 82.60 & 76.90 & 74.00 \\
**paraphrase-multilingual-mpnet-base-v2** & 78.55 & 76.85 & **83.00** & 77.70 & 79.03 \\ \hline (KLUE-RoBERTa-base) KLUE+KSTS+STSb & 87.65 & 88.30 & 67.05 & 71.50 & 78.63 \\
**(KLUE-RoBERTa-base) KLUE+KSTS+STS** & **87.35** & **88.50** & 67.60 & 72.15 & 78.90 \\ (KLUE-RoBERTa-base) KLUE+KSTS+STSb+STS & 87.15 & 88.30 & 66.35 & 70.80 & 78.15 \\ \hline
**Our Hybrid Approach** & 85.60 & 87.35 & 80.55 & **79.30** & **83.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of existing multilingual embedding models (the first five models) and our embedding models trained with different datasets. Note our hybrid approach uses two small monolingual models. terms of total model size, encoding time, time for finding the most similar sentences, and average accuracy. Note that no efficient techniques, such as numba ([[https://numba.py](https://numba.py)]([https://numba.py](https://numba.py)) data.org/, accessed on 2 May 2023), were used in this process. As a result, the absolute values presented may not be meaningful. The elapsed times are only provided for a relative comparison. It is interesting that the Knowledge Distillation paper showed that the multilingual approach outperformed the Korean monolingual approach for Korean test sets, which is opposite to our study's results. Instead, our results are somewhat in line with the study using two monolingual models for intent classification and slot filling [20], which showed they were better than multilingual models for the same task. It is also interesting that we can make models work faster while maintaining higher accuracy by combining small models.