# An Efficient Document Retrieval for Korean Open-Domain Question Answering Based on ColBERT
## 1 Introduction  
Open-domain question answering (ODQA) aims to answer queries based on large knowledge corpora, like the web, without explicit evidence. The typical framework for ODQA is structured as a two-stage process, consisting of a retriever and a reader [1]. The retriever selects a candidate set relevant to a given question from a large corpus, and the reader predicts answers to the question from the retrieved set. The performance of the retriever is typically crucial for the overall QA performance, as it determines the quality of the candidate set. Therefore, there has been extensive research conducted to enhance the retriever's performance [1; 2; 3]. Traditional retrievers mainly use sparse representations through TF-IDF and BM25 [2; 3], often encountering term-mismatch problems due to the difference in terms used in the query and those found in the documents. Furthermore, there are limitations in their performance, as it is difficult to reflect the semantic relationship between documents and queries. Recently, the emergence of dense retrieval utilizing deep learning has shown significant improvements in search performance. Pre-trained language model-based search, particularly BERT [4], has been very effective. However, BERT-based search requires supplying each query-document pair through a large neural network to calculate relevance scores, increasing the computational cost and inference time by tens of thousands of milliseconds (ms)compared to previous approaches [5]. Although BERT-based search has brought significant performance improvements, it has also led to substantial computational costs. To address this issue, varied research on retriever models and techniques that pursue a balance between accuracy, computational cost, and latency have been conducted [6]. In particular, the model for ODQA deals with exceptionally vast datasets, leading to the challenge of retrievers taking considerable time to find question-relevant information or documents. The Learning Index for Learning Passage Retrieval (LIDER) [7] achieves a balance between search speed and accuracy during training by dynamically adapting the corpus index, as opposed to locality-sensitive hashing (LSH) [8] and inverted file (IVF) [9]. While LSH and IVF involve research efforts utilizing efficient approximate nearest neighbor (ANN) methods for enhanced search speed, they often suffer from significant degradation in search accuracy [10]. ColBERT is an innovative ranking model designed to enhance retrieval by efficiently adapting deep language models (specifically BERT) for document ranking. It employs a novel late-interaction architecture that independently encodes queries and documents using BERT, followed by a cost-effective interaction step to model their fine-grained similarity. This approach not only leverages the power of deep language models but also speeds up query processing by enabling pre-computation of document representations. ColBERT's efficiency makes it competitive with existing BERT-based models, outperforming non-BERT baselines while being significantly faster and more resource-efficient. While ColBERT presents itself as a highly efficient retrieval model, its application in the Korean language context remains limited. Research on natural language processing in Korean has not gained as much widespread attention as it has in English, leading to Korean being often referred to as a language with limited resources [11]. Consequently, this results in a shortage of Korean ODQA data, and the scale of the available data is indeed limited. In this paper, we construct a large-scale dataset for training Korean ODQA, surpassing the existing machine reading comprehension (MRC) datasets. We conduct an analysis of our data, including different question types. Additionally, we apply the efficient ColBERT model to our dataset, conducting comparative experiments with conventional retrieval methods to validate the effectiveness of an efficient retrieval model on a large-scale Korean dataset.