Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3643-3650

Passage re-ranking model with autoencoder pre-training and multi-representation interaction

Zhang Kang
Chen Ming
Gu Fan
School of Information, Shanghai Ocean University, Shanghai 201306, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0165

Abstract

In the tasks of passage re-ranking, recent studies propose late interaction architectures based on bi-encoders for faster computation. Since these models independently encode queries and passages during training and inference, the performance of the ranking model heavily relies on the embedding quality of the encoder. Moreover, some multi-vector late-interaction approaches, which calculate text similarity by summing the maximum similarities between character vectors, may encounter partial matching issues. To address these limitations, this paper proposed a pre-training method called replacement paragraph prediction(RPP). It adopted a partially connected autoencoder architecture and employed a task similar to ELECTRA's replacement token prediction to enable the pre-trained model to establish semantic relationships between given queries and passages, thus enhancing its representational capacity. Regarding the improvement of interaction methods, it designed a new late-interaction paradigm. It used different attention mechanisms to guide different text representations for the passages to be ranked. It dynamically fused these representations and computes similarity with the query vector through dot product, providing a lower complexity and finer granularity in interaction. Experiments on the MS MACRO passages ranking dataset demonstrate that the proposed model outperforms ColBERT and PreTTR on the MRR@10 metric under different training conditions. When using knowledge distillation, the proposed model achieves performance comparable to that of the teacher model, and reduces the sorting time on GPUs and a CPUs.

Foundation Support

上海市科技创新计划项目(20dz1203800)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0165
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Algorithm Research & Explore
Pages: 3643-3650
Serial Number: 1001-3695(2023)12-018-3643-08

Publish History

[2023-07-04] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

张康, 陈明, 顾凡. 自编码器预训练和多表征交互的段落重排序模型 [J]. 计算机应用研究, 2023, 40 (12): 3643-3650. (Zhang Kang, Chen Ming, Gu Fan. Passage re-ranking model with autoencoder pre-training and multi-representation interaction [J]. Application Research of Computers, 2023, 40 (12): 3643-3650. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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