## 7 Conclusions  
In this paper, we present an effective hybrid approach for embedding Korean and English sentences in order to find the most similar sentence to a given query. Our main idea is to use two small but effective monolingual models, one for each language, and to choose the appropriate model based on the query sentence. For the Korean sentence embedding model, we use the small pre-trained model (KLUE-roberta-small) for fast encoding and fine-tune it on the KLUE-STS and KSTS datasets. To speed up the process of finding the most similar sentence using cosine similarity, we apply a principal component analysis to reduce the dimensionality by half. The English sentence embedding model is carefully chosen based on experimental results and model size. To verify the effectiveness of our approach, we constructed two types of datasets: one created by artificial intelligence across 34 topics and another created by humans. Our experimental results demonstrate that our hybrid approach outperforms existing multilingual and monolingual approaches in terms of average accuracy, encoding time, and time for finding semantically similar sentences on both CPU and GPU. We expect that our approach will have broad applications in natural language processing, including artificial intelligence education. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Multilingual SBERT Models**} & \multirow{2}{*}{**Test Sets**} & \multicolumn{4}{c}{**Elapsed Time (Seconds)**} & \multirow{2}{*}{**Accuracy**} \\ \cline{3-4} \cline{5-6}  & & **ENC-GPU** & **NNS-GPU** & **ENC-CPU** & **NNS-CPU** & **(\%)** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Huffon/sentence-klue-roberta-base \\ (Total Model Size: 443 MB) \\ \end{tabular} } & GPT-ko & 0.0102 & 0.0727 & 0.0324 & 0.0890 & 85.45 \\  & Paraph-ko & 0.0097 & 0.0725 & 0.0309 & 0.0884 & 89.00 \\  & GPT-en & 0.0104 & 0.0728 & 0.0349 & 0.0882 & 55.25 \\  & Paraph-en & 0.0100 & 0.0732 & 0.0365 & 0.0887 & 70.95 \\ \cline{2-6}  & Average & 0.0101 & 0.0728 & 0.0337 & 0.0886 & 75.16 \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} paraphrase-multilingual-mpnet-base-v2 \\ (Total Model Size: 1.11 GB) \\ \end{tabular} } & GPT-ko & 0.0098 & 0.0731 & 0.0320 & 0.0896 & 78.55 \\  & Paraph-ko & 0.0100 & 0.0732 & 0.0327 & 0.0893 & 76.85 \\  & GPT-en & 0.0100 & 0.0734 & 0.0312 & 0.0895 & 83.00 \\  & Paraph-en & 0.0099 & 0.0731 & 0.0268 & 0.0828 & 77.70 \\ \cline{2-6}  & Average & 0.0099 & 0.0732 & 0.0307 & 0.0878 & 79.03 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} **Our Hybrid Approach (Korean+English)** \\ \end{tabular} } & GPT-ko & 0.0055 & 0.0380 & 0.0167 & 0.0475 & 85.60 \\  & Paraph-ko & 0.0057 & 0.0384 & 0.0162 & 0.0466 & 87.35 \\ \cline{1-1} \cline{2-6}  & GPT-en & 0.0086 & 0.0380 & 0.0115 & 0.0429 & 80.55 \\ \cline{1-1} \cline{2-6}  & Paraph-en & 0.0084 & 0.0379 & 0.0123 & 0.0431 & 79.30 \\ \cline{1-1} \cline{2-6}  & Average & **0.0071** & **0.0381** & **0.0142** & **0.0450** & **83.20** \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of sentence encoding and nearest neighbor search times for models with an average accuracy of 75% or higher, using different hardware configurations. Sentence encoding time per sentence (ENC) and nearest neighbor search time per sentence (NNS) are measured in seconds. **Author Contributions:** Conceptualization, Y.P. and Y.S. ; methodology, Y.P. and Y.S. ; software, Y.P. and Y.S. ; validation, Y.P. and Y.S. ; investigation, Y.P. and Y.S. ; data curation, Y.P. and Y.S. ; writing--original draft preparation, Y.P. and Y.S. ; writing--review and editing, Y.P. and Y.S. ; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by an Incheon National University Research Grant in 2022. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.