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Contrastive Refinement for Dense Retrieval Inference in the Open-Domain Question Answering Task

Author

Listed:
  • Qiuhong Zhai

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • 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)

  • Chenyun Liu

    (Shanghai Municipal Big Data Center, Shanghai 200444, China)

Abstract

In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, neglecting the importance of the passage side. We believe that both the query and passage sides are equally important and should be considered for improved OpenQA performance. In this paper, we propose a contrastive pseudo-labeled data constructed around passages and queries separately. We employ an improved pseudo-relevance feedback (PRF) algorithm with a knowledge-filtering strategy to enrich the semantic information in dense representations. Additionally, we proposed an Auto Text Representation Optimization Model (AOpt) to iteratively update the dense representations. Experimental results demonstrate that our methods effectively optimize dense representations, making them more distinguishable in dense retrieval, thus improving the OpenQA system’s overall performance.

Suggested Citation

  • Qiuhong Zhai & Wenhao Zhu & Xiaoyu Zhang & Chenyun Liu, 2023. "Contrastive Refinement for Dense Retrieval Inference in the Open-Domain Question Answering Task," Future Internet, MDPI, vol. 15(4), pages 1-14, March.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:4:p:137-:d:1113490
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