基于RPRS的超长查询和文档检索:一种高效的基于转换的重新排序器

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Arian Askari, Suzan Verberne, Amin Abolghasemi, Wessel Kraaij, Gabriella Pasi
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引用次数: 0

摘要

在信息检索中,对超长查询和文档进行检索是一项众所周知且具有挑战性的任务,通常称为按文档查询(QBD)检索。在以前的工作中,专门设计的可以处理长输入序列的Transformer模型在QBD任务中没有显示出很高的有效性。我们提出了一种基于新型比例R相关性核心(RPRS)的R - ranker来计算查询与前k个候选文档之间的相关性分数。我们的广泛评估表明,在五个不同的数据集上,RPRS比最先进的模型获得了明显更好的结果。此外,RPRS非常高效,因为所有文档都可以在查询时间之前进行预处理、嵌入和索引,这使我们的重新排序器具有复杂度为O (N)的优势,其中N是查询和候选文档中的句子总数。此外,我们的方法解决了QBD检索任务中训练资源不足的问题,因为它不需要大量的训练数据,并且只有三个范围有限的参数,即使有少量的标记数据,也可以通过网格搜索进行优化。我们的详细分析表明,RPRS受益于覆盖候选文档和查询的完整长度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retrieval for Extremely Long Queries and Documents with RPRS: a Highly Efficient and Effective Transformer-based Re-Ranker
Retrieval with extremely long queries and documents is a well-known and challenging task in information retrieval and is commonly known as Query-by-Document (QBD) retrieval. Specifically designed Transformer models that can handle long input sequences have not shown high effectiveness in QBD tasks in previous work. We propose a R e-Ranker based on the novel P roportional R elevance S core (RPRS) to compute the relevance score between a query and the top-k candidate documents. Our extensive evaluation shows RPRS obtains significantly better results than the state-of-the-art models on five different datasets. Furthermore, RPRS is highly efficient since all documents can be pre-processed, embedded, and indexed before query time which gives our re-ranker the advantage of having a complexity of O ( N ) where N is the total number of sentences in the query and candidate documents. Furthermore, our method solves the problem of the low-resource training in QBD retrieval tasks as it does not need large amounts of training data, and has only three parameters with a limited range that can be optimized with a grid search even if a small amount of labeled data is available. Our detailed analysis shows that RPRS benefits from covering the full length of candidate documents and queries.
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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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