A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering

Wenhao Zhu, Xiaoyu Zhang, Qiuhong Zhai and Chenyun Liu ()
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Wenhao Zhu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Xiaoyu Zhang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Qiuhong Zhai: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Chenyun Liu: Shanghai Municipal Big Data Center, Shanghai 200433, China

Future Internet, 2023, vol. 15, issue 5, 1-14

Abstract: In the two-stage open-domain question answering (OpenQA) systems, the retriever identifies a subset of relevant passages, which the reader then uses to extract or generate answers. However, the performance of OpenQA systems is often hindered by issues such as short and semantically ambiguous queries, making it challenging for the retriever to find relevant passages quickly. This paper introduces Hybrid Text Generation-Based Query Expansion (HTGQE), an effective method to improve retrieval efficiency. HTGQE combines large language models with Pseudo-Relevance Feedback techniques to enhance the input for generative models, improving text generation speed and quality. Building on this foundation, HTGQE employs multiple query expansion generators, each trained to provide query expansion contexts from distinct perspectives. This enables the retriever to explore relevant passages from various angles for complementary retrieval results. As a result, under an extractive and generative QA setup, HTGQE achieves promising results on both Natural Questions (NQ) and TriviaQA (Trivia) datasets for passage retrieval and reading tasks.

Keywords: query expansion; pseudo-relevance feedback; text generation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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