## 1 Introduction  
Sentence-BERT (SBERT) [1] has emerged as one of the most efficient BERT-based [2] sentence embedding approaches. SBERT not only outperforms other approaches such as GloVe [3], InferSent [4], and Universal Sentence Encoder [5] in semantic textual similarity tasks but also solves the problem of slow inference time of cross-encoder (BERT)-based sentence embedding approaches. SBERT typically involves using pre-trained models such as BERT-base, BERT-large, RoBERTa-base, or RoBERTa-large [6], which are fine-tuned on datasets specific to downstream tasks such as semantic textual similarity.  
While most SBERT models focus on the English language, there is a need to embed both English and Korean sentences. To the best of our knowledge, one of the best approaches for embedding both Korean and English sentences using SBERT is through the use of the \"Knowledge Distillation\" [7] method. This method involves a teacher model and a student model, where the student model is trained to imitate the teacher model using translated sentences. Surprisingly, the authors of this approach stated that it outperforms even state-of-the-art Korean monolingual models for Korean semantic textual similarity tasks.  
In this paper, we present a novel and efficient approach for embedding both Korean and English. Instead of using a single multilingual model that maps both language sentences into the same embedding space, our main idea is to prepare each small and efficient monolingual model (Korean and English) by selecting appropriate pre-trained models and training datasets. During inference, we select the appropriate monolingual model based on the language of a query sentence. To create an efficient Korean model, we use the KLUE-roberta-small [8] pre-trained model and fine-tune it on public semantic textual similarity datasets. We then use PCA to reduce the dimensionality of the resulting vectors for efficient inference. For the efficient English model, we select the model that is best suited for our purposes. Figure 1 shows the overview of our approach.  
Note that our research focuses on developing a model for identifying the most semantically similar \"conversational\" sentences, such as questions or commands. To verify the effectiveness of our approach, we construct new test sets composed of conversational sentences. Based on these test sets, we demonstrate that our multiple monolingual models can more accurately and quickly identify the most similar sentences than existing multilingual models. We anticipate that our approach will fulfill the needs of various artificial intelligence applications, including chatbot development, natural language processing education [9; 10; 11], and others.  
The main contributions of our paper are as follows:  
* First, we introduce two types of new conversational test sets to evaluate sentence similarity methods. These sets capture a wider range of conversational contexts (Section 3).
* Second, we propose a sentence embedding model specifically designed for Korean conversational sentences. To develop this model, we select a pre-trained model and fine-tune it on a Korean or Korean-translated corpus (Section 4).
* Third, we compare existing public SBERT models to identify the most efficient English sentence embedding model for this task (Section 5).
* Finally, we present a hybrid approach for embedding both Korean and English sentences, which outperforms existing multilingual approaches in terms of accuracy, elapsed time, and model size (Section 6).