晚期交互模型的匹配机制和标记剪枝分析

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Qi Liu, Gang Guo, Jiaxin Mao, Zhicheng Dou, Ji-Rong Wen, Hao Jiang, Xinyu Zhang, Zhao Cao
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引用次数: 0

摘要

随着预训练语言模型的发展,密集检索模型已成为依赖精确匹配和稀疏词袋表示的传统检索模型的有前途的替代品。与大多数使用双编码器将每个查询或文档编码成一个稠密向量的稠密检索模型不同,最近提出的后期交互多向量模型(即 ColBERT 和 COIL)通过使用所有标记嵌入来表示文档和查询,并使用最大和运算对其相关性进行建模,从而实现了最先进的检索效果。然而,这些细粒度表示法可能会给实际搜索系统带来不可接受的存储开销。在本研究中,我们系统地分析了这些后期交互模型的匹配机制,结果表明最大和运算在很大程度上依赖于共现信号和文档中的一些重要词语。基于这些发现,我们提出了几种简单的文档剪枝方法来减少存储开销,并比较了不同剪枝方法对不同后期交互模型的效果。我们还利用查询剪枝方法来进一步减少检索延迟。我们在域内和域外数据集上进行了广泛的实验,结果表明,所使用的一些剪枝方法可以显著提高这些后期交互模型的效率,而不会对其检索效果造成实质性损害。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT and COIL) achieve state-of-the-art retrieval effectiveness by using all token embeddings to represent documents and queries and modeling their relevance with a sum-of-max operation. However, these fine-grained representations may cause unacceptable storage overhead for practical search systems. In this study, we systematically analyze the matching mechanism of these late-interaction models and show that the sum-of-max operation heavily relies on the co-occurrence signals and some important words in the document. Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models. We also leverage query pruning methods to further reduce the retrieval latency. We conduct extensive experiments on both in-domain and out-domain datasets and show that some of the used pruning methods can significantly improve the efficiency of these late-interaction models without substantially hurting their retrieval effectiveness.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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