# A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
## 1 Introduction  
Open-Domain Question Answering (OpenQA) aims to answer questions without pre-specified domains, including end-to-end question-answering systems and pipeline-based systems. The former regards the OpenQA system as a whole for joint training, while the latter decomposes the system into multiple components for separate training. Currently, pipeline-based OpenQA systems are more common, such as those developed after 2017, which generally follow the two-stage Retriever-Reader (R2) architecture proposed by [PERSON] et al. [1]. This architecture first retrieves a small subset of passages based on the query, and then the reader extracts or generates answers from the retrieved passages. Therefore, when the retriever retrieves more relevant passages, the reader is more likely to extract the answer, and the performance of the OpenQA system can be improved. However, queries are often short, incompletely expressed semantically, and full of ambiguities. So the model cannot fully understand the query's intent, thus retrieving irrelevant passages, and readers extract or generate incorrect answers. To improve retrieval efficiency, we use the query expansion technique. Query expansion is the process of reformulating a given query to improve retrieval performance in information retrieval operations, especially in the context of query comprehension, which expands the initial query to match additional relevant passages. Traditional query expansion methods include (1) linguistics-based, (2) corpus-based, (3) search log-based, and (4) network-based approaches. These methods all involve selecting expansion terms that are semantically similar to the original terms from existing knowledge resources or large corpora. Another important method is Pseudo-Relevance Feedback (PRF), which assumes that top-ranked passages at the retrieval stage contain relevant signals and leverages thesesignals to modify initial queries, reducing the impact of lexical mismatches between queries and passages and improving search efficiency. BERT [2], T5 [3], and other transformer-based deep language models combined with PRF techniques have significantly improved performance in different search tasks [4; 5] compared with traditional PRF methods such as Rocchio, association model, RM3 and KL expansion models. The two keys of query expansion are the types of expansion terms and the selection methods. (1) The expansion terms extracted by the above methods are more inclined to be semantically close to the items in the original query. However, choosing only semantically related words as expansion terms is insufficient for retrieving the most relevant passages. Utilizing diverse contextual information can help queries retrieve relevant passages from different angles for complementarity. (2) Regarding the method for selecting expansion terms, traditional statistical methods and modern neural networks can be used to select terms from relevant passages. Recently, generative models have played a significant role in OpenQA, performing exceptionally as readers and query expansion generators [6; 7]. However, due to the closed-book nature of these models, the generation process can be challenging. Providing some reference materials for the generative models and transforming them into \"open-book\" generation can speed up the generation process and improve the quality of the generated results [8; 9]. In this paper, we propose HTGQE ([[https://github.com/XY2323819551/TGQE_code](https://github.com/XY2323819551/TGQE_code)]([https://github.com/XY2323819551/TGQE_code](https://github.com/XY2323819551/TGQE_code)), accessed on 27 April 2023), a Hybrid Text Generation-based Query Expansion method to improve retrieval efficiency. We improve the quality of query expansion terms in two ways: the type of expansion generated and the way it is generated. We utilize the generation model as query expansion generator, combining PRF techniques in information retrieval with text generation technologies.