# An Efficient Document Retrieval for Korean Open-Domain Question Answering Based on ColBERT
## 7 Conclusions  
In this paper, we propose an efficient Korean document retrieval method for open-domain question answering using the ColBERT structure. We construct a Korean dataset for open-domain question answering and evaluate the performance of the proposed method on this dataset. The experimental results show that our proposed KoBERT-based ColBERT achieves about 13% higher performance in MRR@10 compared to the traditional term-based retrieval model BM25 and reduces the search speed by approximately 3.7 times. KoBERT-based ColBERT shows about 8% lower performance in MRR@10 compared to dense retrieval KoBERT, but the search speed is shortened by more than ten times. Furthermore, when utilizing the KoSBERT-based ColBERT method, the sentence-embedding ability for both questions and documents is improved with the use of KoSBERT, achieving the best search performance and the shortest search time.  
In this study, the ColBERT method is not only applicable to open-domain question answering tasks but also holds potential for situations requiring both search accuracy and speed simultaneously, such as search engines and recommendation systems. Additionally,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MRR@10** & **MRR@100** & **Recall@50** & **Recall@200** & **Latency (ms)** \\ \hline ColBERT & & & & & \\ w/KLUE- & 66.04 & 66.40 & 86.66 & 90.26 & 886 \\ RoBERTa & & & & & \\ ColBERT & 67.29 & 68.07 & 87.16 & 90.50 & 1240 \\ w/KoBigBird & 67.91 & 68.24 & 86.99 & **90.79** & 1322 \\ BERT & & & & & \\ ColBERT & **68.15** & **68.48** & **87.26** & 90.63 & **873** \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._given that search performance is improved when utilizing sentence embeddings trained to capture semantic similarity between sentences, future research will investigate approaches to enhance embeddings for effectively capturing semantic similarity between query and document, with the goal of further improving search performance.  
Conceptualization, B.K., Y.K. and Y.S.; methodology, B.K. and Y.K.; software, B.K. and Y.K.; validation, B.K. and Y.K.; formal analysis, B.K. and Y.K.; investigation, B.K. and Y.K.; resources, Y.S.; data curation, B.K. and Y.K.; writing--original draft preparation, B.K. and Y.K.; writing--review and editing, Y.S.; visualization, B.K.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1G1A1012766).  
Not applicable.  
The data and related information used in this study can be found at the [[https://github.com/movie112/ColBERTforKorean](https://github.com/movie112/ColBERTforKorean)]([https://github.com/movie112/ColBERTforKorean](https://github.com/movie112/ColBERTforKorean)).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BERT & Bidirectional Encoder Representations from Transformers \\ ColBERT & Contextualized Late Interaction over BERT \\ IR & Information Retrieval \\ TF-IDF & Term Frequency-Inverse Document Frequency \\ MRR & Mean Reciprocal Rank \\ SBERT & Sentence-BERT \\ NLI & Natural Language Inference \\ STS & Semantic Textual Similarity \\ KLUE & Korean Language Understanding Evaluation \\ RoBERTa & A Robustly Optimized BERT Pre-training Approach \\ \end{tabular}